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With the advances in scientific foundations and technological implementations, optical metrology has become versatile problem-solving backbones in manufacturing, fundamental research, and engineering applications, such as quality control, nondestructive testing, experimental mechanics, and biomedicine. In recent years, deep learning, a subfield of machine learning, is emerging as a powerful tool to address problems by learning from data, largely driven by the availability of massive datasets, enhanced computational power, fast data storage, and novel training algorithms for the deep neural network. It is currently promoting increased interests and gaining extensive attention for its utilization in the field of optical metrology. Unlike the traditional “physics-based” approach, deep-learning-enabled optical metrology is a kind of “data-driven” approach, which has already provided numerous alternative solutions to many challenging problems in this field with better performances. In this review, we present an overview of the current status and the latest progress of deep-learning technologies in the field of optical metrology. We first briefly introduce both traditional image-processing algorithms in optical metrology and the basic concepts of deep learning, followed by a comprehensive review of its applications in various optical metrology tasks, such as fringe denoising, phase retrieval, phase unwrapping, subset correlation, and error compensation. The open challenges faced by the current deep-learning approach in optical metrology are then discussed. Finally, the directions for future research are outlined.

Recovering the high-resolution three-dimensional (3D) surface of an object from a single frame image has been the ultimate goal long pursued in fringe projection profilometry (FPP). The color fringe projection method is one of the technologies with the most potential towards such a goal due to its three-channel multiplexing properties. However, the associated color imbalance, crosstalk problems, and compromised coding strategy remain major obstacles to overcome. Inspired by recent successes of deep learning for FPP, we propose a single-shot absolute 3D shape measurement with deep-learning-based color FPP. Through “learning” on extensive data sets, the properly trained neural network can “predict” the high-resolution, motion-artifact-free, crosstalk-free absolute phase directly from one single color fringe image. Compared with the traditional approach, our method allows for more accurate phase retrieval and more robust phase unwrapping. Experimental results demonstrate that the proposed approach can provide high-accuracy single-frame absolute 3D shape measurement for complicated objects.

We present a differential front end design for improving the performance of short-range low-cost Doppler radars for vital sign detection with application to automotive driver safety systems, health monitoring, and security screening. By using dual helical antennas each with a 40-degree beamwidth, it is possible to illuminate the body in two adjacent locations to perform a differential measurement. Since only one of the beams illuminates the heart, the baseband signal from the second radar is used for motion cancellation. We demonstrate this approach using a custom-designed dual helical antenna and simple directconversion radar circuits implemented in microstrip operating at 2.460 GHz and 2.510 GHz., respectively. Heart rate data is shown for detection distances of 0.5 meter showing reduction in background motion noise compared to data from traditional single radar unit design.

The Internet of Things (IoT), inspired by the tremendous growth of connected heterogeneous devices, has pioneered the notion of smart city. Various components, i.e., smart transportation, smart community, smart healthcare, smart grid, etc. which are integrated within smart city architecture aims to enrich the quality of life (QoL) of urban citizens. However, real-time processing requirements and exponential data growth withhold smart city realization. Therefore, herein we propose a Big Data analytics (BDA)-embedded experimental architecture for smart cities. Two major aspects are served by the BDA-embedded smart city. Firstly, it facilitates exploitation of urban Big Data (UBD) in planning, designing, and maintaining smart cities. Secondly, it occupies BDA to manage and process voluminous UBD to enhance the quality of urban services. Three tiers of the proposed architecture are liable for data aggregation, real-time data management, and service provisioning. Moreover, offline and online data processing tasks are further expedited by integrating data normalizing and data filtering techniques to the proposed work. By analyzing authenticated datasets, we obtained the threshold values required for urban planning and city operation management. Performance metrics in terms of online and offline data processing for the proposed dual-node Hadoop cluster is obtained using aforementioned authentic datasets. Throughput and processing time analysis performed with regard to existing works guarantee the performance superiority of the proposed work. Hence, we can claim the applicability and reliability of implementing proposed BDA-embedded smart city architecture in the real world.

Orthogonal signal-division multiplexing (OSDM) is a promising modulation scheme that provides a generalized framework to unify orthogonal frequency-division multiplexing (OFDM) and single-carrier frequency-domain equalization. By partitioning each data block into vectors, it allows for a flexible configuration to trade off resource management flexibility with peak-to-average power ratio. In this paper, an OSDM system is proposed for underwater acoustic communications. The channel Doppler effect after front-end resampling is modeled as a common time-varying phase on all propagation paths. It leads to a special signal distortion structure in the OSDM system, namely, intervector interference, which is analogous to the intercarrier interference in the conventional OFDM system. To counteract the related performance degradation, the OSDM receiver performs iterative detection, integrating joint channel impulse response and phase estimation, equalization, and decoding in a loop. Meanwhile, to avoid inversion of large matrices in channel equalization, frequency-domain per-vector equalization is designed, which can significantly reduce the computational complexity. Furthermore, the performance of the proposed OSDM system is evaluated through both numerical simulations and a field experiment, and its reliability over underwater acoustic channels is confirmed. Index Terms-Orthogonal signal-division multiplexing (OSDM), time-varying channels, turbo equalization, underwater acoustic communications. I. INTRODUCTION U NDERWATER acoustic (UWA) channels are considered as one of the most challenging communication media in use [1]. Specifically, UWA channels exhibit limited available bandwidth, typically of the order of 10 kHz for medium-range links, due to the frequency-dependent transmission loss. Also, UWA channels suffer from long multipath spread and severe Manuscript

The bipartite matching strategy is first applied in the graph theory to optimise the resource allocation in a device-to-device (D2D) underlaying cellular network. With each D2D group reusing the resource of one particular cellular user, the proposed scheme minimises the cross-interference between D2D users and traditional cellular users with polynomial complexity. Simulation shows that the optimal system capacity boundary is achieved by the proposed scheme and D2D communication shows its great potential in increasing system capacity.

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