Showing papers by "Southeast University published in 2019"

Electrospinning and Electrospun Nanofibers: Methods, Materials, and Applications

Abstract: Electrospinning is a versatile and viable technique for generating ultrathin fibers. Remarkable progress has been made with regard to the development of electrospinning methods and engineering of electrospun nanofibers to suit or enable various applications. We aim to provide a comprehensive overview of electrospinning, including the principle, methods, materials, and applications. We begin with a brief introduction to the early history of electrospinning, followed by discussion of its principle and typical apparatus. We then discuss its renaissance over the past two decades as a powerful technology for the production of nanofibers with diversified compositions, structures, and properties. Afterward, we discuss the applications of electrospun nanofibers, including their use as "smart" mats, filtration membranes, catalytic supports, energy harvesting/conversion/storage components, and photonic and electronic devices, as well as biomedical scaffolds. We highlight the most relevant and recent advances related to the applications of electrospun nanofibers by focusing on the most representative examples. We also offer perspectives on the challenges, opportunities, and new directions for future development. At the end, we discuss approaches to the scale-up production of electrospun nanofibers and briefly discuss various types of commercial products based on electrospun nanofibers that have found widespread use in our everyday life.

Accessing From the Sky: A Tutorial on UAV Communications for 5G and Beyond

Abstract: Unmanned aerial vehicles (UAVs) have found numerous applications and are expected to bring fertile business opportunities in the next decade. Among various enabling technologies for UAVs, wireless communication is essential and has drawn significantly growing attention in recent years. Compared to the conventional terrestrial communications, UAVs’ communications face new challenges due to their high altitude above the ground and great flexibility of movement in the 3-D space. Several critical issues arise, including the line-of-sight (LoS) dominant UAV-ground channels and induced strong aerial-terrestrial network interference, the distinct communication quality-of-service (QoS) requirements for UAV control messages versus payload data, the stringent constraints imposed by the size, weight, and power (SWAP) limitations of UAVs, as well as the exploitation of the new design degree of freedom (DoF) brought by the highly controllable 3-D UAV mobility. In this article, we give a tutorial overview of the recent advances in UAV communications to address the above issues, with an emphasis on how to integrate UAVs into the forthcoming fifth-generation (5G) and future cellular networks. In particular, we partition our discussion into two promising research and application frameworks of UAV communications, namely UAV-assisted wireless communications and cellular-connected UAVs, where UAVs are integrated into the network as new aerial communication platforms and users, respectively. Furthermore, we point out promising directions for future research.

Highly Accurate Machine Fault Diagnosis Using Deep Transfer Learning

Abstract: We develop a novel deep learning framework to achieve highly accurate machine fault diagnosis using transfer learning to enable and accelerate the training of deep neural network. Compared with existing methods, the proposed method is faster to train and more accurate. First, original sensor data are converted to images by conducting a Wavelet transformation to obtain time-frequency distributions. Next, a pretrained network is used to extract lower level features. The labeled time-frequency images are then used to fine-tune the higher levels of the neural network architecture. This paper creates a machine fault diagnosis pipeline and experiments are carried out to verify the effectiveness and generalization of the pipeline on three main mechanical datasets including induction motors, gearboxes, and bearings with sizes of 6000, 9000, and 5000 time series samples, respectively. We achieve state-of-the-art results on each dataset, with most datasets showing test accuracy near 100%, and in the gearbox dataset, we achieve significant improvement from 94.8% to 99.64%. We created a repository including these datasets located at mlmechanics.ics.uci.edu.

Large Intelligent Surface-Assisted Wireless Communication Exploiting Statistical CSI

Abstract: Large intelligent surface (LIS)-assisted wireless communications have drawn attention worldwide. With the use of low-cost LIS on building walls, signals can be reflected by the LIS and sent out along desired directions by controlling its phases, thereby providing supplementary links for wireless communication systems. In this paper, we evaluate the performance of an LIS-assisted large-scale antenna system by formulating a tight upper bound of the ergodic spectral efficiency and investigate the effect of the phase shifts on the ergodic spectral efficiency in different propagation scenarios. In particular, we propose an optimal phase shift design based on the upper bound of the ergodic spectral efficiency and statistical channel state information. Furthermore, we derive the requirement on the quantization bits of the LIS to promise an acceptable spectral efficiency degradation. Numerical results show that using the proposed phase shift design can achieve the maximum ergodic spectral efficiency, and a 2-bit quantizer is sufficient to ensure spectral efficiency degradation of no more than 1 bit/s/Hz.

Wireless Communications with Reconfigurable Intelligent Surface: Path Loss Modeling and Experimental Measurement

Abstract: Reconfigurable intelligent surfaces (RISs) comprised of tunable unit cells have recently drawn significant attention due to their superior capability in manipulating electromagnetic waves. In particular, RIS-assisted wireless communications have the great potential to achieve significant performance improvement and coverage enhancement in a cost-effective and energy-efficient manner, by properly programming the reflection coefficients of the unit cells of RISs. In this paper, free-space path loss models for RIS-assisted wireless communications are developed for different scenarios by studying the physics and electromagnetic nature of RISs. The proposed models, which are first validated through extensive simulation results, reveal the relationships between the free-space path loss of RIS-assisted wireless communications and the distances from the transmitter/receiver to the RIS, the size of the RIS, the near-field/far-field effects of the RIS, and the radiation patterns of antennas and unit cells. In addition, three fabricated RISs (metasurfaces) are utilized to further corroborate the theoretical findings through experimental measurements conducted in a microwave anechoic chamber. The measurement results match well with the modeling results, thus validating the proposed free-space path loss models for RIS, which may pave the way for further theoretical studies and practical applications in this field.

Binodal, wireless epidermal electronic systems with in-sensor analytics for neonatal intensive care

Abstract: INTRODUCTION In neonatal intensive care units (NICUs), continuous monitoring of vital signs is essential, particularly in cases of severe prematurity. Current monitoring platforms require multiple hard-wired, rigid interfaces to a neonate’s fragile, underdeveloped skin and, in some cases, invasive lines inserted into their delicate arteries. These platforms and their wired interfaces pose risks for iatrogenic skin injury, create physical barriers for skin-to-skin parental/neonate bonding, and frustrate even basic clinical tasks. Technologies that bypass these limitations and provide additional, advanced physiological monitoring capabilities would directly address an unmet clinical need for a highly vulnerable population. RATIONALE It is now possible to fabricate wireless, battery-free vital signs monitoring systems based on ultrathin, “skin-like” measurement modules. These devices can gently and noninvasively interface onto the skin of neonates with gestational ages down to the edge of viability. Four essential advances in engineering science serve as the foundations for this technology: (i) schemes for wireless power transfer, low-noise sensing, and high-speed data communications via a single radio-frequency link with negligible absorption in biological tissues; (ii) efficient algorithms for real-time data analytics, signal processing, and dynamic baseline modulation implemented on the sensor platforms themselves; (iii) strategies for time-synchronized streaming of wireless data from two separate devices; and (iv) designs that enable visual inspection of the skin interface while also allowing magnetic resonance imaging and x-ray imaging of the neonate. The resulting systems can be much smaller in size, lighter in weight, and less traumatic to the skin than any existing alternative. RESULTS We report the realization of this class of NICU monitoring technology, embodied as a pair of devices that, when used in a time-synchronized fashion, can reconstruct full vital signs information with clinical-grade precision. One device mounts on the chest to capture electrocardiograms (ECGs); the other rests on the base of the foot to simultaneously record photoplethysmograms (PPGs). This binodal system captures and continuously transmits ECG, PPG, and (from each device) skin temperature data, yielding measurements of heart rate, heart rate variability, respiration rate, blood oxygenation, and pulse arrival time as a surrogate of systolic blood pressure. Successful tests on neonates with gestational ages ranging from 28 weeks to full term demonstrate the full range of functions in two level III NICUs. The thin, lightweight, low-modulus characteristics of these wireless devices allow for interfaces to the skin mediated by forces that are nearly an order of magnitude smaller than those associated with adhesives used for conventional hardware in the NICU. This reduction greatly lowers the potential for iatrogenic injuries. CONCLUSION The advances outlined here serve as the basis for a skin-like technology that not only reproduces capabilities currently provided by invasive, wired systems as the standard of care, but also offers multipoint sensing of temperature and continuous tracking of blood pressure, all with substantially safer device-skin interfaces and compatibility with medical imaging. By eliminating wired connections, these platforms also facilitate therapeutic skin-to-skin contact between neonates and parents, which is known to stabilize vital signs, reduce morbidity, and promote parental bonding. Beyond use in advanced hospital settings, these systems also offer cost-effective capabilities with potential relevance to global health.

Observation of the nonlinear Hall effect under time-reversal-symmetric conditions

Abstract: The electrical Hall effect is the production, upon the application of an electric field, of a transverse voltage under an out-of-plane magnetic field. Studies of the Hall effect have led to important breakthroughs, including the discoveries of Berry curvature and topological Chern invariants1,2. The internal magnetization of magnets means that the electrical Hall effect can occur in the absence of an external magnetic field2; this 'anomalous' Hall effect is important for the study of quantum magnets2-7. The electrical Hall effect has rarely been studied in non-magnetic materials without external magnetic fields, owing to the constraint of time-reversal symmetry. However, only in the linear response regime-when the Hall voltage is linearly proportional to the external electric field-does the Hall effect identically vanish as a result of time-reversal symmetry; the Hall effect in the nonlinear response regime is not subject to such symmetry constraints8-10. Here we report observations of the nonlinear Hall effect10 in electrical transport in bilayers of the non-magnetic quantum material WTe2 under time-reversal-symmetric conditions. We show that an electric current in bilayer WTe2 leads to a nonlinear Hall voltage in the absence of a magnetic field. The properties of this nonlinear Hall effect are distinct from those of the anomalous Hall effect in metals: the nonlinear Hall effect results in a quadratic, rather than linear, current-voltage characteristic and, in contrast to the anomalous Hall effect, the nonlinear Hall effect results in a much larger transverse than longitudinal voltage response, leading to a nonlinear Hall angle (the angle between the total voltage response and the applied electric field) of nearly 90 degrees. We further show that the nonlinear Hall effect provides a direct measure of the dipole moment10 of the Berry curvature, which arises from layer-polarized Dirac fermions in bilayer WTe2. Our results demonstrate a new type of Hall effect and provide a way of detecting Berry curvature in non-magnetic quantum materials.

Reduced default mode network functional connectivity in patients with recurrent major depressive disorder.

Abstract: Major depressive disorder (MDD) is common and disabling, but its neuropathophysiology remains unclear. Most studies of functional brain networks in MDD have had limited statistical power and data analysis approaches have varied widely. The REST-meta-MDD Project of resting-state fMRI (R-fMRI) addresses these issues. Twenty-five research groups in China established the REST-meta-MDD Consortium by contributing R-fMRI data from 1,300 patients with MDD and 1,128 normal controls (NCs). Data were preprocessed locally with a standardized protocol before aggregated group analyses. We focused on functional connectivity (FC) within the default mode network (DMN), frequently reported to be increased in MDD. Instead, we found decreased DMN FC when we compared 848 patients with MDD to 794 NCs from 17 sites after data exclusion. We found FC reduction only in recurrent MDD, not in first-episode drug-naive MDD. Decreased DMN FC was associated with medication usage but not with MDD duration. DMN FC was also positively related to symptom severity but only in recurrent MDD. Exploratory analyses also revealed alterations in FC of visual, sensory-motor, and dorsal attention networks in MDD. We confirmed the key role of DMN in MDD but found reduced rather than increased FC within the DMN. Future studies should test whether decreased DMN FC mediates response to treatment. All R-fMRI indices of data contributed by the REST-meta-MDD consortium are being shared publicly via the R-fMRI Maps Project.

Machine-learning reprogrammable metasurface imager.

Abstract: Conventional microwave imagers usually require either time-consuming data acquisition, or complicated reconstruction algorithms for data post-processing, making them largely ineffective for complex in-situ sensing and monitoring. Here, we experimentally report a real-time digital-metasurface imager that can be trained in-situ to generate the radiation patterns required by machine-learning optimized measurement modes. This imager is electronically reprogrammed in real time to access the optimized solution for an entire data set, realizing storage and transfer of full-resolution raw data in dynamically varying scenes. High-accuracy image coding and recognition are demonstrated in situ for various image sets, including hand-written digits and through-wall body gestures, using a single physical hardware imager, reprogrammed in real time. Our electronically controlled metasurface imager opens new venues for intelligent surveillance, fast data acquisition and processing, imaging at various frequencies, and beyond. Conventional imagers require time-consuming data acquisition, or complicated reconstruction algorithms for data post-processing. Here, the authors demonstrate a real-time digital-metasurface imager that can be trained in-situ to show high accuracy image coding and recognition for various image sets.

Radiative sky cooling: Fundamental principles, materials, and applications

Abstract: Radiative sky cooling cools an object on the earth by emitting thermal infrared radiation to the cold universe through the atmospheric window (8–13 μm). It consumes no electricity and has great potential to be explored for cooling of buildings, vehicles, solar cells, and even thermal power plants. Radiative sky cooling has been explored in the past few decades but limited to nighttime use only. Very recently, owing to the progress in nanophotonics and metamaterials, daytime radiative sky cooling to achieve subambient temperatures under direct sunlight has been experimentally demonstrated. More excitingly, the manufacturing of the daytime radiative sky cooling material by the roll-to-roll process makes large-scale deployment of the technology possible. This work reviews the fundamental principles of radiative sky cooling as well as the recent advances, from both materials and systems point of view. Potential applications in different scenarios are reviewed with special attention to technology viability and benefits. As the energy situation and environmental issues become more and more severe in the 21st century, radiative sky cooling can be explored for energy saving in buildings and vehicles, mitigating the urban heat island effect, resolving water and environmental issues, achieving more efficient power generation, and even fighting against the global warming problem.

Secrecy Rate Maximization for Intelligent Reflecting Surface Assisted Multi-Antenna Communications

Abstract: We investigate transmission optimization for intelligent reflecting surface (IRS) assisted multi-antenna systems from the physical-layer security perspective. The design goal is to maximize the system secrecy rate subject to the source transmit power constraint and the unit modulus constraints imposed on phase shifts at the IRS. To solve this complicated non-convex problem, we develop an efficient alternating algorithm where the solutions to the transmit covariance of the source and the phase shift matrix of the IRS are achieved in closed form and semi-closed form, respectively. The convergence of the proposed algorithm is guaranteed theoretically. Simulation results validate the performance advantage of the proposed optimized design.

Double-negative-index ceramic aerogels for thermal superinsulation

Abstract: Ceramic aerogels are attractive for thermal insulation but plagued by poor mechanical stability and degradation under thermal shock. In this study, we designed and synthesized hyperbolic architectured ceramic aerogels with nanolayered double-pane walls with a negative Poisson’s ratio (−0.25) and a negative linear thermal expansion coefficient (−1.8 × 10 −6 per °C). Our aerogels display robust mechanical and thermal stability and feature ultralow densities down to ~0.1 milligram per cubic centimeter, superelasticity up to 95%, and near-zero strength loss after sharp thermal shocks (275°C per second) or intense thermal stress at 1400°C, as well as ultralow thermal conductivity in vacuum [~2.4 milliwatts per meter-kelvin (mW/m·K)] and in air (~20 mW/m·K). This robust material system is ideal for thermal superinsulation under extreme conditions, such as those encountered by spacecraft.

A molecular perovskite solid solution with piezoelectricity stronger than lead zirconate titanate.

Abstract: Piezoelectric materials produce electricity when strained, making them ideal for different types of sensing applications. The most effective piezoelectric materials are ceramic solid solutions in which the piezoelectric effect is optimized at what are termed morphotropic phase boundaries (MPBs). Ceramics are not ideal for a variety of applications owing to some of their mechanical properties. We synthesized piezoelectric materials from a molecular perovskite (TMFM)x(TMCM)1–xCdCl3 solid solution (TMFM, trimethylfluoromethyl ammonium; TMCM, trimethylchloromethyl ammonium, 0 ≤ x ≤ 1), in which the MPB exists between monoclinic and hexagonal phases. We found a composition for which the piezoelectric coefficient d33 is ~1540 picocoulombs per newton, comparable to high-performance piezoelectric ceramics. The material has potential applications for wearable piezoelectric devices.

Spatial–Temporal Recurrent Neural Network for Emotion Recognition

Abstract: In this paper, we propose a novel deep learning framework, called spatial–temporal recurrent neural network (STRNN), to integrate the feature learning from both spatial and temporal information of signal sources into a unified spatial–temporal dependency model. In STRNN, to capture those spatially co-occurrent variations of human emotions, a multidirectional recurrent neural network (RNN) layer is employed to capture long-range contextual cues by traversing the spatial regions of each temporal slice along different directions. Then a bi-directional temporal RNN layer is further used to learn the discriminative features characterizing the temporal dependencies of the sequences, where sequences are produced from the spatial RNN layer. To further select those salient regions with more discriminative ability for emotion recognition, we impose sparse projection onto those hidden states of spatial and temporal domains to improve the model discriminant ability. Consequently, the proposed two-layer RNN model provides an effective way to make use of both spatial and temporal dependencies of the input signals for emotion recognition. Experimental results on the public emotion datasets of electroencephalogram and facial expression demonstrate the proposed STRNN method is more competitive over those state-of-the-art methods.

Rational Design of Crystalline Covalent Organic Frameworks for Efficient CO 2 Photoreduction with H 2 O

Abstract: Solar energy-driven conversion of CO2 into fuels with H2 O as a sacrificial agent is a challenging research field in photosynthesis. Herein, a series of crystalline porphyrin-tetrathiafulvalene covalent organic frameworks (COFs) are synthesized and used as photocatalysts for reducing CO2 with H2 O, in the absence of additional photosensitizer, sacrificial agents, and noble metal co-catalysts. The effective photogenerated electrons transfer from tetrathiafulvalene to porphyrin by covalent bonding, resulting in the separated electrons and holes, respectively, for CO2 reduction and H2 O oxidation. By adjusting the band structures of TTCOFs, TTCOF-Zn achieved the highest photocatalytic CO production of 12.33 μmol with circa 100 % selectivity, along with H2 O oxidation to O2 . Furthermore, DFT calculations combined with a crystal structure model confirmed the structure-function relationship. Our work provides a new sight for designing more efficient artificial crystalline photocatalysts.

Accessing From The Sky: A Tutorial on UAV Communications for 5G and Beyond

Abstract: Unmanned aerial vehicles (UAVs) have found numerous applications and are expected to bring fertile business opportunities in the next decade. Among various enabling technologies for UAVs, wireless communication is essential and has drawn significantly growing attention in recent years. Compared to the conventional terrestrial communications, UAVs' communications face new challenges due to their high altitude above the ground and great flexibility of movement in the three-dimensional (3D) space. Several critical issues arise, including the line-of-sight (LoS) dominant UAV-ground channels and resultant strong aerial-terrestrial network interference, the distinct communication quality of service (QoS) requirements for UAV control messages versus payload data, the stringent constraints imposed by the size, weight and power (SWAP) limitations of UAVs, as well as the exploitation of the new design degree of freedom (DoF) brought by the highly controllable 3D UAV mobility. In this paper, we give a tutorial overview of the recent advances in UAV communications to address the above issues, with an emphasis on how to integrate UAVs into the forthcoming fifth-generation (5G) and future cellular networks. In particular, we partition our discussions into two promising research and application frameworks of UAV communications, namely UAV-assisted wireless communications and cellular-connected UAVs,where UAVs serve as aerial communication platforms and users, respectively. Furthermore, we point out promising directions for future research and investigation.

Global Prevalence of Hypertension in Children: A Systematic Review and Meta-analysis.

Abstract: Importance Reliable estimates of the prevalence of childhood hypertension serve as the basis for adequate prevention and treatment. However, the prevalence of childhood hypertension has rarely been synthesized at the global level. Objective To conduct a systematic review and meta-analysis to assess the prevalence of hypertension in the general pediatric population. Data Sources PubMed, MEDLINE, Embase, Global Health, and Global Health Library were searched from inception until June 2018, using search terms related to hypertension (hypertensionORhigh blood pressureORelevated blood pressure), children (childrenORadolescents), and prevalence (prevalenceORepidemiology). Study Selection Studies that were conducted in the general pediatric population and quantified the prevalence of childhood hypertension were eligible. Included studies had blood pressure measurements from at least 3 separate occasions. Data Extraction and Synthesis Two authors independently extracted data. Random-effects meta-analysis was used to derive the pooled prevalence. Variations in the prevalence estimates in different subgroups, including age group, sex, setting, device, investigation period, BMI group, World Health Organization region and World Bank region, were examined by subgroup meta-analysis. Meta-regression was used to establish the age-specific prevalence of childhood hypertension and to assess its secular trend. Main Outcomes and Measures Prevalence of childhood hypertension overall and by subgroup. Results A total of 47 articles were included in the meta-analysis. The pooled prevalence was 4.00% (95% CI, 3.29%-4.78%) for hypertension, 9.67% (95% CI, 7.26%-12.38%) for prehypertension, 4.00% (95% CI, 2.10%-6.48%) for stage 1 hypertension, and 0.95% (95% CI, 0.48%-1.57%) for stage 2 hypertension in children 19 years and younger. In subgroup meta-analyses, the prevalence of childhood hypertension was higher when measured by aneroid sphygmomanometer (7.23% vs 4.59% by mercury sphygmomanometer vs 2.94% by oscillometric sphygmomanometer) and among overweight and obese children (15.27% and 4.99% vs 1.90% among normal-weight children). A trend of increasing prevalence of childhood hypertension was observed during the past 2 decades, with a relative increasing rate of 75% to 79% from 2000 to 2015. In 2015, the prevalence of hypertension ranged from 4.32% (95% CI, 2.79%-6.63%) among children aged 6 years to 3.28% (95% CI, 2.25%-4.77%) among those aged 19 years and peaked at 7.89% (95% CI, 5.75%-10.75%) among those aged 14 years. Conclusions and Relevance This study provides a global estimation of childhood hypertension prevalence based on blood pressure measurements in at least 3 separate visits. More high-quality epidemiologic investigations on childhood hypertension are still needed.

Programmable time-domain digital-coding metasurface for non-linear harmonic manipulation and new wireless communication systems.

Abstract: Optical non-linear phenomena are typically observed in natural materials interacting with light at high intensities, and they benefit a diverse range of applications from communication to sensing. However, controlling harmonic conversion with high efficiency and flexibility remains a major issue in modern optical and radio-frequency systems. Here, we introduce a dynamic time-domain digital-coding metasurface that enables efficient manipulation of spectral harmonic distribution. By dynamically modulating the local phase of the surface reflectivity, we achieve accurate control of different harmonics in a highly programmable and dynamic fashion, enabling unusual responses, such as velocity illusion. As a relevant application, we propose and realize a novel architecture for wireless communication systems based on the time-domain digital-coding metasurface, which largely simplifies the architecture of modern communication systems, at the same time yielding excellent performance for real-time signal transmission. The presented work, from new concept to new system, opens new pathways in the application of metamaterials to practical technology.

Model-Driven Deep Learning for Physical Layer Communications

Abstract: Intelligent communication is gradually becoming a mainstream direction. As a major branch of machine learning, deep learning (DL) has been applied in physical layer communications and has demonstrated an impressive performance improvement in recent years. However, most existing works related to DL focus on data-driven approaches, which consider the communication system as a black box and train it by using a huge volume of data. Training a network requires sufficient computing resources and extensive time, both of which are rarely found in communication devices. By contrast, model-driven DL approaches combine communication domain knowledge with DL to reduce the demand for computing resources and training time. This article discusses the recent advancements in model-driven DL approaches in physical layer communications, including transmission schemes, receiver design, and channel information recovery. Several open issues for future research are also highlighted.

Smart metasurface with self-adaptively reprogrammable functions

Abstract: Intelligence at either the material or metamaterial level is a goal that researchers have been pursuing. From passive to active, metasurfaces have been developed to be programmable to dynamically and arbitrarily manipulate electromagnetic (EM) wavefields. However, the programmable metasurfaces require manual control to switch among different functionalities. Here, we put forth a smart metasurface that has self-adaptively reprogrammable functionalities without human participation. The smart metasurface is capable of sensing ambient environments by integrating an additional sensor(s) and can adaptively adjust its EM operational functionality through an unmanned sensing feedback system. As an illustrative example, we experimentally develop a motion-sensitive smart metasurface integrated with a three-axis gyroscope, which can adjust self-adaptively the EM radiation beams via different rotations of the metasurface. We develop an online feedback algorithm as the control software to make the smart metasurface achieve single-beam and multibeam steering and other dynamic reactions adaptively. The proposed metasurface is extendable to other physical sensors to detect the humidity, temperature, illuminating light, and so on. Our strategy will open up a new avenue for future unmanned devices that are consistent with the ambient environment.

A General Two-Step Strategy–Based High-Throughput Screening of Single Atom Catalysts for Nitrogen Fixation

Abstract: Electrocatalytic or photocatalytic N2 reduction holds great promise for green and sustainable NH3 production under ambient conditions, where an efficient catalyst plays a crucial role but remains a long‐standing challenge. Here, a high‐throughput screening of catalysts for N2 reduction among (nitrogen‐doped) graphene‐supported single atom catalysts is performed based on a general two‐step strategy. 10 promising candidates with excellent performance are extracted from 540 systems. Most strikingly, a single W atom embedded in graphene with three C atom coordination (W1C3) exhibits the best performance with an extremely low onset potential of 0.25 V. This study not only provides a series of promising catalysts for N2 fixation, but also paves a new way for the rational design of catalysts for N2 fixation under ambient conditions.

DeepNIS: Deep Neural Network for Nonlinear Electromagnetic Inverse Scattering

Abstract: Nonlinear electromagnetic (EM) inverse scattering is a quantitative and super-resolution imaging technique, in which more realistic interactions between the internal structure of scene and EM wavefield are taken into account in the imaging procedure, in contrast to conventional tomography. However, it poses important challenges arising from its intrinsic strong nonlinearity, ill-posedness, and expensive computational costs. To tackle these difficulties, we, for the first time to our best knowledge, exploit a connection between the deep neural network (DNN) architecture and the iterative method of nonlinear EM inverse scattering. This enables the development of a novel DNN-based methodology for nonlinear EM inverse problems (termed here DeepNIS). The proposed DeepNIS consists of a cascade of multilayer complex-valued residual convolutional neural network modules. We numerically and experimentally demonstrate that the DeepNIS outperforms remarkably conventional nonlinear inverse scattering methods in terms of both the image quality and computational time. We show that DeepNIS can learn a general model approximating the underlying EM inverse scattering system. It is expected that the DeepNIS will serve as powerful tool in treating highly nonlinear EM inverse scattering problems over different frequency bands, which are extremely hard and impractical to solve using conventional inverse scattering methods.

Joint Beamforming and Power Allocation for Satellite-Terrestrial Integrated Networks With Non-Orthogonal Multiple Access

Abstract: In this paper, we propose a joint optimization design for a non-orthogonal multiple access (NOMA)-based satellite-terrestrial integrated network (STIN), where a satellite multicast communication network shares the millimeter wave spectrum with a cellular network employing NOMA technology. By assuming that the satellite uses multibeam antenna array and the base station employs uniform planar array, we first formulate a constrained optimization problem to maximize the sum rate of the STIN while satisfying the constraint of per-antenna transmit power and quality-of-service requirements of both satellite and cellular users. Since the formulated optimization problem is NP-hard and mathematically intractable, we develop a novel user pairing scheme so that more than two users can be grouped in a cluster to exploit the NOMA technique. Based on the user clustering, we further propose to transform the non-convex problem into an equivalent convex one, and present an iterative penalty function-based beamforming (BF) scheme to obtain the BF weight vectors and power coefficients with fast convergence. Simulation results confirm the effectiveness and superiority of the proposed approach in comparison with the existing works.

Iterative Learning Control for a Flapping Wing Micro Aerial Vehicle Under Distributed Disturbances

Abstract: This paper addresses a flexible micro aerial vehicle (MAV) under spatiotemporally varying disturbances, which is composed of a rigid body and two flexible wings. Based on Hamilton’s principle, a distributed parameter system coupling in bending and twisting, is modeled. Two iterative learning control (ILC) schemes are designed to suppress the vibrations in bending and twisting, reject the distributed disturbances and regulate the displacement of the rigid body to track a prescribed constant trajectory. At the basis of composite energy function, the boundedness and the learning convergence are proved for the closed-loop MAV system. Simulation results are provided to illustrate the effectiveness of the proposed ILC laws.

Deep CNN-Based Channel Estimation for mmWave Massive MIMO Systems

Abstract: For millimeter wave (mmWave) massive multiple-input multiple-output (MIMO) systems, hybrid processing architecture is usually used to reduce the complexity and cost, which poses a very challenging issue in channel estimation. In this paper, deep convolutional neural network (CNN) is employed to address this problem. We first propose a spatial-frequency CNN (SF-CNN) based channel estimation exploiting both the spatial and frequency correlation, where the corrupted channel matrices at adjacent subcarriers are input into the CNN simultaneously. Then, exploiting the temporal correlation in time-varying channels, a spatial-frequency-temporal CNN (SFT-CNN) based approach is developed to further improve the accuracy. Moreover, we design a spatial pilot-reduced CNN (SPR-CNN) to save spatial pilot overhead for channel estimation, where channels in several successive coherence intervals are grouped and estimated by a channel estimation unit with memory. Numerical results show that the proposed SF-CNN and SFT-CNN based approaches outperform the non-ideal minimum mean-squared error (MMSE) estimator but with reduced complexity, and achieve the performance close to the ideal MMSE estimator that is very difficult to be implemented in practical situations. They are also robust to different propagation scenarios. The SPR-CNN based approach achieves comparable performance to SF-CNN and SFT-CNN based approaches while only requires about one-third of spatial pilot overhead at the cost of complexity. The results in this paper clearly demonstrate that deep CNN can efficiently exploit channel correlation to improve the estimation performance for mmWave massive MIMO systems.

6G Technologies: Key Drivers, Core Requirements, System Architectures, and Enabling Technologies

Abstract: The key drivers of 6G result not only from the challenges and performance limits that 5G presents but also from the technology-driven paradigm shift and the continuous evolution of wireless networks. Intelligent driving and industry revolutions create core requirements for 6G that will lead to service classes of ubiquitous mobile ultrabroadband (uMUB), ultrahighspeed-with-low-latency communications (uHSLLC), and ultrahigh data density (uHDD).