Showing papers by "University of Electro-Communications published in 2021"

QoS-Guarantee Resource Allocation for Multibeam Satellite Industrial Internet of Things With NOMA

Abstract: The traditional ground industrial Internet of Things (IIoT) cannot supply wireless interconnections anywhere due to its small-scale communication coverage. In this article, a multibeam satellite IIoT in Ka-band is proposed to realize wide-area coverage and long-distance transmissions, which uses nonorthogonal multiple access (NOMA) for each beam to improve transmission rate. To guarantee Quality of Service (QoS) for the satellite IIoT, the beam power is optimized to match the theoretical transmission rate with the service rate. The NOMA transmission rate for each beam is maximized by optimizing the power allocation proportion of each node subject to the constraints of the total power for the beam and the minimal transmission rate for each node within the beam. Satellite-ground integrated IIoT is proposed to use the ground cellular network to supplement the satellite coverage in the blocked areas. The power allocation and network selection for the integrated IIoT are proposed to decrease the transmission cost. Simulation results are provided to validate the superiority of employing NOMA in the satellite IIoT and show higher transmission performance for the QoS-guarantee resource allocation.

Current status of space gravitational wave antenna DECIGO and B-DECIGO

Abstract: The Deci-hertz Interferometer Gravitational Wave Observatory (DECIGO) is a future Japanese space mission with a frequency band of 0.1 Hz to 10 Hz. DECIGO aims at the detection of primordial gravitational waves, which could have been produced during the inflationary period right after the birth of the Universe. There are many other scientific objectives of DECIGO, including the direct measurement of the acceleration of the expansion of the Universe, and reliable and accurate predictions of the timing and locations of neutron star/black hole binary coalescences. DECIGO consists of four clusters of observatories placed in heliocentric orbit. Each cluster consists of three spacecraft, which form three Fabry-Perot Michelson interferometers with an arm length of 1000 km. Three DECIGO clusters will be placed far from each other, and the fourth will be placed in the same position as one of the other three to obtain correlation signals for the detection of primordial gravitational waves. We plan to launch B-DECIGO, which is a scientific pathfinder for DECIGO, before DECIGO in the 2030s to demonstrate the technologies required for DECIGO, as well as to obtain fruitful scientific results to further expand multi-messenger astronomy.

Elegant Construction of ZnIn2S4/BiVO4 Hierarchical Heterostructures as Direct Z-Scheme Photocatalysts for Efficient CO2 Photoreduction.

Abstract: The ZnIn2S4/BiVO4 heterostructures were elegantly designed through assembling ZnIn2S4 nanosheets onto the surface of BiVO4 decahedrons. This composite photocatalyst exhibits efficient photocatalytic conversion of CO2 into CO with a detectable amount of CH4 in the presence of water vapor. An electron spin-resonance spectroscopy (ESR) technique and density function theory (DFT) calculation affirm the direct Z-scheme structure in ZnIn2S4/BiVO4. The larger surface photovoltage (SPV) change and the longer liquid photoluminescence (PL) lifetime of the heterostructure, compared to the individual ZnIn2S4 and BiVO4 components, demonstrate that the Z-scheme structure can effectively promote the recombination of the photogenerated holes in the valence band (VB) of the ZnIn2S4 nanosheet with the electrons in the conduction band (CB) of the decahedral BiVO4 and lead to the abundant electrons surviving in the CB of ZnIn2S4 and holes in the VB of BiVO4, thus enhancing photocatalytic CO2 reduction performance. This study may make a potential contribution to the rational construction and deep understanding of the underlying mechanism of direct Z-schemes for advanced photocatalytic activity.

Reinforcement Learning-Based Multislot Double-Threshold Spectrum Sensing With Bayesian Fusion for Industrial Big Spectrum Data

Abstract: With the rapid increase of industrial systems, industrial spectrum is stepping into the era of big data, and at the same time spectrum resources are facing serious shortage. Cognitive industrial system (CIS) based on cognitive radio can improve spectrum utilization by accessing the idle spectrum licensed to primary user. However, the CIS must find enough idle channels by performing spectrum sensing. In this article, a reinforcement learning-based multislot double-threshold spectrum sensing with Bayesian fusion is proposed to sense industrial big spectrum data, which can find required idle channels faster while guaranteeing spectrum sensing performance. Double thresholds are set to guarantee both high detection probability and spectrum access probability, and weighed energy detection is proposed to maximize detection probability when the energy statistic falls into the confusion area between the double thresholds. Bayesian fusion is proposed to get a final decision on the channel availability by combining the local sensing decisions of all the time slots. A prediction and selection algorithm for idle channels is proposed to predict the idle probability of each channel and find required idle channels from the sorted channel set. From simulation results, the proposed spectrum sensing scheme outperforms cooperative spectrum sensing and energy detection, which can predict idle channels accurately and get needed idle channels with fewer sensing operations.

Computation Offloading in Beyond 5G Networks: A Distributed Learning Framework and Applications

Abstract: Facing the trend of merging wireless communications and multi-access edge computing (MEC), this article studies computation offloading in beyond fifth generation networks. To address the technical challenges originating from the uncertainties and the sharing of limited resource in an MEC system, we formulate the computation offloading problem as a multi-agent Markov decision process, for which a distributed learning framework is proposed. We present a case study on resource orchestration in computation offloading to showcase the potential of an online distributed reinforcement learning algorithm developed under the proposed framework. Experimental results demonstrate that our learning algorithm outperforms the benchmark resource orchestration algorithms. Furthermore, we outline the research directions worth in-depth investigation to minimize the time cost, which is one of the main practical issues that prevent the implementation of the proposed distributed learning framework.

Point Cloud Video Streaming: Challenges and Solutions

Abstract: Volumetric video (or hologram video), the medium for representing natural content in VR/AR/MR, is presumably the next generation of video technology and a typical use case for 5G and beyond wireless communications. To realize volumetric video applications, efficient volumetric video streaming is in critical demand. This article responds to the challenges of and proposes solutions to wireless transmission systems of point cloud video, which is the most popular and favored way to represent volumetric media and significantly differs from the other types of videos. In particular, we first introduce point cloud video technology and its applications, and then discuss the challenges of and solutions to point cloud video streaming, including encoding, tiling, viewing angle prediction, decoding, quality assessment and transmission optimization. Furthermore, we explain a prototype of a MPEG DASH-based point cloud video streaming system as a preliminary study, along with more simulation results to verify its performance. Finally, we identify future research directions for providing high-quality point cloud video streaming.

The Perseus ALMA Chemistry Survey (PEACHES). I. The Complex Organic Molecules in Perseus Embedded Protostars

Abstract: To date, about two dozen low-mass embedded protostars exhibit rich spectra with lines of complex organic molecule (COM). These protostars seem to possess different enrichment in COMs. However, the statistics of COM abundance in low-mass protostars are limited by the scarcity of observations. This study introduces the Perseus ALMA Chemistry Survey (PEACHES), which aims at unbiasedly characterizing the chemistry of COMs toward the embedded (Class 0/I) protostars in the Perseus molecular cloud. Of 50 embedded protostars surveyed, 58% of them have emission from COMs. A 56%, 32%, and 40% of the protostars have CH$_3$OH, CH$_3$OCHO, and N-bearing COMs, respectively. The detectability of COMs depends neither on the averaged continuum brightness temperature, a proxy of the H$_2$ column density, nor on the bolometric luminosity and the bolometric temperature. For the protostars with detected COMs, CH$_3$OH has a tight correlation with CH$_3$CN, spanning more than two orders of magnitude in column densities normalized by the continuum brightness temperature, suggesting a chemical relation between CH$_3$OH and CH$_3$CN and a large chemical diversity in the PEACHES samples at the same time. A similar trend with more scatter is also found between all identified COMs, hinting at a common chemistry for the sources with COMs. The correlation between COMs is insensitive to the protostellar properties, such as the bolometric luminosity and the bolometric temperature. The abundance of larger COMs (CH$_3$OCHO and CH$_3$OCH$_3$) relative to that of smaller COMs (CH$_3$OH and CH$_3$CN) increases with the inferred gas column density, hinting at an efficient production of complex species in denser envelopes.

Bismuth Vacancy-Induced Efficient CO2 Photoreduction in BiOCl Directly from Natural Air: A Progressive Step toward Photosynthesis in Nature.

Abstract: Photocatalytic CO2 conversion into carbonaceous fuels through artificial photosynthesis is beneficial to global warming mitigation and renewable resource generation. However, a high cost is always required by special CO2-capturing devices for efficient artificial photosynthesis. For achieving highly efficient photocatalytic CO2 reduction (PCR) directly from natural air, we report rose-like BiOCl that is rich in Bi vacancies (VBi) assembled by nanosheets with almost fully exposed active {001} facets. These rose-like BiOCl with VBi assemblies provide considerable adsorption and catalytic sites, which hoists the CO2 capture and reduction capabilities, and thus expedites the PCR to a superior value of 21.99 μmol·g-1·h-1 CO generation under a 300 W Xe lamp within 5 h from natural air. The novel design and construction of a photocatalyst in this work could break through the conventional PCR system requiring compression and purification for CO2, dramatically reduce expenses, and open up new possibilities for the practical application of artificial photosynthesis.

Over 40-W Electric Power and Optical Data Transmission Using an Optical Fiber

Abstract: Simultaneous over 40-W electric power and optical data transmission using an optical fiber is demonstrated for optically powered remote antenna units in future mobile communication networks. In this article, to further increase the delivered electric power by power-over-fiber link using a double-clad fiber, we improve the link design to extract a higher feed light power from the double-clad fiber output. Furthermore, to increase the electric power for driving remote antenna units, we employ a specially customized photovoltaic power converter that directly converts optical power into electric power. The photovoltaic power converter can input a feed light with power of over 20 W and has a high optical-to-electrical conversion efficiency of over 50%. As a result, the combination of the improved power-over-fiber link design and the use of the photovoltaic power converter successfully achieves the electric power delivery of up to 43.7 W. This is the highest electric power delivery demonstration by power-over-fiber with optical data signals using a single optical fiber, to the best of the authors’ knowledge.

Superconducting-like Heat Current: Effective Cancellation of Current-Dissipation Trade-Off by Quantum Coherence.

Abstract: Quantum coherence is a useful resource for increasing the speed and decreasing the irreversibility of quantum dynamics. Because of this feature, coherence is used to enhance the performance of various quantum information processing devices beyond the limitations set by classical mechanics. However, when we consider thermodynamic processes, such as energy conversion in nanoscale devices, it is still unclear whether coherence provides similar advantages. Here we establish a universal framework, clarifying how coherence affects the speed and irreversibility in thermodynamic processes described by the Lindblad master equation, and give general rules for when coherence enhances or reduces the performance of thermodynamic devices. Our results show that a proper use of coherence enhances the heat current without increasing dissipation; i.e., coherence can reduce friction. In particular, if the amount of coherence is large enough, this friction becomes virtually zero, realizing a superconducting-like "dissipation-less" heat current. Since our framework clarifies a general relation among coherence, energy flow, and dissipation, it can be applied to many branches of science from quantum information theory to biology. As an application to energy science, we construct a quantum heat engine cycle that exceeds the power-efficiency trade-off bound on classical engines and effectively attains the Carnot efficiency with finite power in fast cycles.

Robust Edge Computing in UAV Systems via Scalable Computing and Cooperative Computing

Abstract: Unmanned aerial vehicle (UAV) systems are of increasing interest to academia and industry due to their mobility, flexibility, and maneuverability, and are an effective alternative to various uses such as surveillance and mobile edge computing. However, due to their limited computational and communications resources, it is difficult to serve all computation tasks simultaneously. This article tackles this problem by first proposing a scalable aerial computing solution, which is applicable for computation tasks of multiple quality levels, corresponding to different computation workloads and computation results of distinct performance. It opens up the possibility to maximally improve the overall computing performance with limited computational and communications resources. To meet the demands for timely video analysis that exceed the computing power of a UAV, we propose an aerial video streaming enabled cooperative computing solution, namely, UAVideo, which streams videos from a UAV to ground servers. As a complement to scalable aerial computing, UAVideo minimizes the video streaming time under the constraints on UAV trajectory, video features, and communications resources. Simulation results reveal the substantial advantages of the proposed solutions. Furthermore, we highlight relevant directions for future research.

The effect of water on colloidal quantum dot solar cells.

Abstract: Almost all surfaces sensitive to the ambient environment are covered by water, whereas the impacts of water on surface-dominated colloidal quantum dot (CQD) semiconductor electronics have rarely been explored. Here, strongly hydrogen-bonded water on hydroxylated lead sulfide (PbS) CQD is identified. The water could pilot the thermally induced evolution of surface chemical environment, which significantly influences the nanostructures, carrier dynamics, and trap behaviors in CQD solar cells. The aggravation of surface hydroxylation and water adsorption triggers epitaxial CQD fusion during device fabrication under humid ambient, giving rise to the inter-band traps and deficiency in solar cells. To address this problem, meniscus-guided-coating technique is introduced to achieve dense-packed CQD solids and extrude ambient water, improving device performance and thermal stability. Our works not only elucidate the water involved PbS CQD surface chemistry, but may also achieve a comprehensive understanding of the impact of ambient water on CQD based electronics.

Edge computing-based joint client selection and networking scheme for federated learning in vehicular IoT

Abstract: In order to support advanced vehicular Internet-of-Things (IoT) applications, information exchanges among different vehicles are required to find efficient solutions for catering to different application requirements in complex and dynamic vehicular environments. Federated learning (FL), which is a type of distributed learning technology, has been attracting great interest in recent years as it performs knowledge exchange among different network entities without a violation of user privacy. However, client selection and networking scheme for enabling FL in dynamic vehicular environments, which determines the communication delay between FL clients and the central server that aggregates the models received from the clients, is still under-explored. In this paper, we propose an edge computing-based joint client selection and networking scheme for vehicular IoT. The proposed scheme assigns some vehicles as edge vehicles by employing a distributed approach, and uses the edge vehicles as FL clients to conduct the training of local models, which learns optimal behaviors based on the interaction with environments. The clients also work as forwarder nodes in information sharing among network entities. The client selection takes into account the vehicle velocity, vehicle distribution, and the wireless link connectivity between vehicles using a fuzzy logic algorithm, resulting in an efficient learning and networking architecture. We use computer simulations to evaluate the proposed scheme in terms of the communication overhead and the information covered in learning.

Virtual Edge: Exploring Computation Offloading in Collaborative Vehicular Edge Computing

Abstract: Vehicular edge computing (VEC) has been a new paradigm to support computation-intensive and latency-sensitive services. However, the scarcity of computational resources is still a challenge. Making efficient use of sporadic idle computational resources on smart vehicles in the vicinity to extend the resource capability of each vehicle is an important research issue. In this paper, we propose Virtual Edge, which is an efficient scheme to utilize free computational resources of multiple vehicles as a virtual server to facilitate collaborative vehicular edge computing. We design a virtual edge formation algorithm that considers both the stability of virtual edge and the computational resources available at the vehicles constituting the virtual edge. The prediction of the link duration between vehicles reduces the number of computation offloading failures caused by unexpected link disconnections. Extensive simulations with realistic vehicle movements are conducted to show the advantage of the proposed scheme over existing baselines in terms of the completion ratio of computation offloading tasks and average task execution time.

Information Freshness-Aware Task Offloading in Air-Ground Integrated Edge Computing Systems

Abstract: This paper investigates an air-ground integrated multi-access edge computing system, which is deployed by an infrastructure provider (InP). Under a business agreement with the InP, a third-party service provider provides computing services to the subscribed mobile users (MUs). MUs compete for the shared spectrum and computing resources over time to achieve their distinctive goals. From the perspective of an MU, we deliberately define the age of update to capture the staleness of information from refreshing computation outcomes. Given the system dynamics, we model the interactions among MUs as a stochastic game. In the Nash equilibrium without cooperation, each MU behaves in accordance with the local system states and conjectures. We can hence transform the stochastic game into a single-agent Markov decision process. As another major contribution, we develop an online deep reinforcement learning (RL) scheme that adopts two separate double deep Q-networks to approximate the Q-factor and the post-decision Q-factor, respectively. The deep RL scheme allows each MU to optimize the behaviours with unknown dynamic statistics. Numerical experiments show that our proposed scheme outperforms the baselines in terms of the average utility under various system conditions.

Penetration of MeV electrons into the mesosphere accompanying pulsating aurorae

Abstract: Pulsating aurorae (PsA) are caused by the intermittent precipitations of magnetospheric electrons (energies of a few keV to a few tens of keV) through wave-particle interactions, thereby depositing most of their energy at altitudes ~ 100 km. However, the maximum energy of precipitated electrons and its impacts on the atmosphere are unknown. Herein, we report unique observations by the European Incoherent Scatter (EISCAT) radar showing electron precipitations ranging from a few hundred keV to a few MeV during a PsA associated with a weak geomagnetic storm. Simultaneously, the Arase spacecraft has observed intense whistler-mode chorus waves at the conjugate location along magnetic field lines. A computer simulation based on the EISCAT observations shows immediate catalytic ozone depletion at the mesospheric altitudes. Since PsA occurs frequently, often in daily basis, and extends its impact over large MLT areas, we anticipate that the PsA possesses a significant forcing to the mesospheric ozone chemistry in high latitudes through high energy electron precipitations. Therefore, the generation of PsA results in the depletion of mesospheric ozone through high-energy electron precipitations caused by whistler-mode chorus waves, which are similar to the well-known effect due to solar energetic protons triggered by solar flares.

DRLE: Decentralized Reinforcement Learning at the Edge for Traffic Light Control in the IoV

Abstract: The Internet of Vehicles (IoV) enables real-time data exchange among vehicles and roadside units and thus provides a promising solution to alleviate traffic jams in the urban area. Meanwhile, better traffic management via efficient traffic light control can benefit the IoV as well by enabling a better communication environment and decreasing the network load. As such, IoV and efficient traffic light control can formulate a virtuous cycle. Edge computing, an emerging technology to provide low-latency computation capabilities at the edge of the network, can further improve the performance of this cycle. However, while the collected information is valuable, an efficient solution for better utilization and faster feedback has yet to be developed for edge-empowered IoV. To this end, we propose a Decentralized Reinforcement Learning at the Edge for traffic light control in the IoV (DRLE). DRLE exploits the ubiquity of the IoV to accelerate traffic data collection and interpretation towards better traffic light control and congestion alleviation. Operating within the coverage of the edge servers, DRLE aggregates data from neighboring edge servers for city-scale traffic light control. DRLE decomposes the highly complex problem of large area control into a decentralized multi-agent problem. We prove its global optima with concrete mathematical reasoning and demonstrate its superiority over several state-of-the-art algorithms via extensive evaluations.

Motion Planning for Dual-Arm Robot Based on Soft Actor-Critic

Abstract: In this paper, a motion planning method based on the Soft Actor-Critic (SAC) is designed for a dual-arm robot with two 7-Degree-of-Freedom (7-DOF) arms so that the robot can effectively avoid self-collision and at the same time can avoid the joint limits and singularities of the arm. The left-arm and right-arm of the dual-arm robot each have a neural network to control its position and orientation. Dual-agent training, distributed training structure, and progressive training environment are used to train neural networks. During the training process, the motion of one arm is regarded as the environment of the other arm, and the two agents are trained at the same time. In the input part of the neural network of the proposed method, all parameters come from the angle of each axis and kinematic calculation, no additional sensors are needed, so the method is easier to transplant to different dual-arm robots. With some appropriate neural network inputs and reward functions design, the robot can perform the expected self-collision avoidance and effectively avoid the joint limits and singularities of the arm. Finally, some experiments of the simulation tests in the Gazebo simulator and actual tests in a laboratory-made dual-arm robot are presented to illustrate the proposed SAC-based motion planning method is feasible and practicable in the avoidance of self-collision, joint limits, and singularities.

Nanoscale Turing patterns in a bismuth monolayer

Abstract: Turing’s reaction–diffusion theory of morphogenesis has been very successful for understanding macroscopic patterns within complex objects ranging from biological systems to sand dunes. However, Turing patterns on microscopic length scales are extremely rare. Here we show that a strained atomic bismuth monolayer assembled on the surface of NbSe2—and subject to interatomic interactions and kinetics—displays Turing patterns. Our reaction–diffusion model produces stripe patterns with a period of five atoms (approximately 2 nm) and domain walls with Y-shaped junctions that bear a striking resemblance to what has been experimentally observed. Our work establishes that Turing patterns can occur at the atomic scale in a hard condensed-matter setting. Macroscale patterns seen in biological systems such as animal coats or skin can be described by Turing’s reaction–diffusion theory. Now Turing patterns are shown to also exist in bismuth monolayers, an exemplary nanoscale atomic system.

Decentralized Radio Resource Adaptation in D2D-U Networks

Abstract: Unlike the conventional device-to-device (D2D) networks, the unlicensed D2D (D2D-U) pairs can not only reuse the licensed channels with the base station (BS) but also share the unlicensed channels with the WiFi stations. One challenge arises from the fact that the co-channel interference on licensed channels and the collision probability on unlicensed channels may cause extra power consumption at the terminals. Accordingly, we first propose a channel access method for the D2D-U pairs on unlicensed channels. Then, a decentralized joint spectrum and power allocation scheme is designed to minimize the power consumption at D2D-U pairs. Different from the existing distributed schemes, the proposed scheme can guarantee the global minimization of power consumption across the D2D-U pairs. Simulation results validate the theoretical analysis and verify the performance from the proposed scheme.

A review of deep-neural automated essay scoring models

Abstract: Automated essay scoring (AES) is the task of automatically assigning scores to essays as an alternative to grading by humans. Although traditional AES models typically rely on manually designed features, deep neural network (DNN)-based AES models that obviate the need for feature engineering have recently attracted increased attention. Various DNN-AES models with different characteristics have been proposed over the past few years. To our knowledge, however, no study has provided a comprehensive review of DNN-AES models while introducing each model in detail. Therefore, this review presents a comprehensive survey of DNN-AES models, describing the main idea and detailed architecture of each model. We classify the AES task into four types and introduce existing DNN-AES models according to this classification.

Towards mmWave V2X in 5G and Beyond to Support Automated Driving

Abstract: Millimeter wave provides high data rates for Vehicle-to-Everything (V2X) communications. This paper motivates millimeter wave to support automated driving and begins by explaining V2X use cases that support automated driving with references to several standardi-zation bodies. The paper gives a classification of existing V2X stand-ards: IEEE802.11p and LTE V2X, along with the status of their com-mercial deployment. Then, the paper provides a detailed assessment on how millimeter wave V2X enables the use case of cooperative perception. The explanations provide detailed rate calculations for this use case and show that millimeter wave is the only technology able to achieve the requirements. Furthermore, specific challenges related to millimeter wave for V2X are described, including cover-age enhancement and beam alignment. The paper concludes with some results from three studies, i.e. IEEE802.11ad (WiGig) based V2X, extension of 5G NR (New Radio) toward mmWave V2X, and prototypes of intelligent street with mmWave V2X.

Hollow InVO4 Nanocuboid Assemblies toward Promoting Photocatalytic N2 Conversion Performance

Abstract: The unique InVO4 mesocrystal superstructure, particularly with cubical skeleton and hollow interior, which consists of numerous nanocube building blocks, closely stacking by stacking, aligning by aligning, and sharing the same crystallographic orientations, is successfully fabricated. The synergy of a reaction-limited aggregation and an Ostwald ripening process is reasonably proposed for the growth of this unique superstructure. Both single-particle surface photovoltage and confocal fluorescence spectroscopy measurements demonstrate that the long-range ordered mesocrystal superstructures can significantly retard the recombination of electron-hole pairs through the creation of a new pathway for anisotropic electron flow along the inter-nanocubes. This promising charge mobility feature of the superstructure greatly contributes to the pronounced photocatalytic performance of the InVO4 mesocrystal toward fixation of N2 into NH3 with the quantum yield of 0.50% at wavelength of 385 nm.

Recent advancements and opportunities of decorated graphitic carbon nitride toward solar fuel production and beyond

Abstract: Recent advances have revealed the potentials of rationally designed graphitic carbon nitride (g-C3N4) for the photocatalytic H2 evolution reaction due to its unique morphological structure and appealing electronic and physicochemical properties. In this review, we introduce the basic principles of the photocatalytic overall water splitting, the advantages and challenges of g-C3N4-based photocatalysts. The unique electronic, crystal structure, surface physicochemical, and adsorption properties of g-C3N4-based photocatalysts are discussed to provide insightful prospects on charge dynamics. This is followed by a comprehensive overview of the recent developments of g-C3N4 for photocatalytic reactions, particularly focusing on various tailoring strategies, which include: (1) heterojunction design, g-C3N4/semiconductor heterojunction, Z-scheme heterojunction, and g-C3N4/metal heterojunction (cocatalyst), (2) functionality design at the atomic level, such as elemental doping, and (3) nanostructure architecting by tuning the dimensionality of g-C3N4 that affects photoactivity. In addition, some fundamental issues and challenges in each of the aforementioned strategies are also highlighted. The advancements of the photo-redox applications toward water splitting, CO2 photoreduction, and N2 photo-fixation are also presented. This review is expected to provide impactful guidelines and ideas to readers in this field on the development of g-C3N4. Moreover, it is a useful information for the development of future design strategies.

Deep Reinforcement Learning for Tiled Aperture Beam Combining in a Simulated Environment

Abstract: Coherent beam combining (CBC) is a method for combining multiple emitters into one high power beam by means of relative phase stabilization. Usually, modulation or interferometric techniques are used to generate an error signal. This is relatively complicated and expensive. Especially in the case of tiled aperture combining the beam profile is usually monitored anyway. This beam profile should contain most of the information necessary for the stabilization as well but is usually not used because it is difficult to explicitly derive the correct actions from just the far-field image. Here we show that it is possible to derive a suitable control policy without any explicit modeling using deep reinforcement learning in a simulated environment.

An efficient clustering-based anonymization scheme for privacy-preserving data collection in IoT based healthcare services

Abstract: The healthcare services industry has seen a huge transformation since the prominent rise of the Internet of Things (IoT). IoT in healthcare services includes a large number of unified and interconnected sensors, and medical devices that generate and exchange sensitive information. Thus, an enormous amount of data is transmitted through the network which raises an alarming concern for the privacy of patient information. Therefore, privacy preserving data collection (PPDC) is on-demand to ensure the privacy of patient data. Several pieces of research on PPDC have been proposed recently. However, the research literatures have fallen short in privacy requirements and are prone to various privacy attacks. In this paper, we propose a novel privacy-preserving data collection scheme for IoT based healthcare services systems. A clustering-based anonymity model is utilized to develop an efficient privacy-preserving scheme to meet privacy requirements and to prevent healthcare IoT from various privacy attacks. We formulated the threat model as client-server-to-user to ensure privacy on both ends. On the client-side, a modified clustering-based k-anonymity model with α-deassociation is used to anonymize the data generated from the IoT nodes. The base-level privacy is then ensured through a bottom-up clustering method which generates clusters of records as per the privacy requirements. On the server-side, the cluster-combination method-UPGMA is utilized to reduce communication costs and to achieve a better level of privacy. The proposed scheme is efficient in tackling privacy attacks such as attribute disclosure, identity disclosure, membership disclosure, sensitivity attacks, similarity attacks, and skewness attacks. The effectiveness and efficiency of the proposed scheme are proven through theoretical and experimental analyses.