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Deploying small cell base stations (SBS) under the coverage area of a macro base station (MBS), and caching popular contents at the SBSs in advance, are effective means to provide high-speed and low-latency services in next generation mobile communication networks. In this paper, we investigate the problem of content caching (CC) and user association (UA) for edge computing. A joint CC and UA optimization problem is formulated to minimize the content download latency. We prove that the joint CC and UA optimization problem is NP-hard. Then, we propose a CC and UA algorithm (JCC-UA) to reduce the content download latency. JCC-UA includes a smart content caching policy (SCCP) and dynamic user association (DUA). SCCP utilizes the exponential smoothing method to predict content popularity and cache contents according to prediction results. DUA includes a rapid association (RA) method and a delayed association (DA) method. Simulation results demonstrate that the proposed JCC-UA algorithm can effectively reduce the latency of user content downloading and improve the hit rates of contents cached at the BSs as compared to several baseline schemes.

In this paper, we propose a novel Electroencephalograph (EEG) emotion recognition method inspired by neuroscience with respect to the brain response to different emotions. The proposed method, denoted by R2G-STNN, consists of spatial and temporal neural network models with regional to global hierarchical feature learning process to learn discriminative spatial-temporal EEG features. To learn the spatial features, a bidirectional long short term memory (BiLSTM) network is adopted to capture the intrinsic spatial relationships of EEG electrodes within brain region and between brain regions, respectively. Considering that different brain regions play different roles in the EEG emotion recognition, a region-attention layer into the R2G-STNN model is also introduced to learn a set of weights to strengthen or weaken the contributions of brain regions. Based on the spatial feature sequences, BiLSTM is adopted to learn both regional and global spatial-temporal features and the features are fitted into a classifier layer for learning emotion-discriminative features, in which a domain discriminator working corporately with the classifier is used to decrease the domain shift between training and testing data. Finally, to evaluate the proposed method, we conduct both subject-dependent and subject-independent EEG emotion recognition experiments on SEED database, and the experimental results show that the proposed method achieves state-of-the-art performance.

Low-light images suffer from low contrast and unclear details, which not only reduces the available information for humans but limits the application of computer vision algorithms. Among the existing enhancement techniques, Retinex-based and learning-based methods are under the spotlight today. In this paper, we bridge the gap between the two methods. First, we propose a novel "generative" strategy for Retinex decomposition, by which the decomposition is cast as a generative problem. Second, based on the strategy, a unified deep framework is proposed to estimate the latent components and perform low-light image enhancement. Third, our method can weaken the coupling relationship between the two components while performing Retinex decomposition. Finally, the RetinexDIP performs Retinex decomposition without any external images, and the estimated illumination can be easily adjusted and is used to perform enhancement. The proposed method is compared with ten state-of-the-art algorithms on seven public datasets, and the experimental results demonstrate the superiority of our method. Code is available at:https://github.com/zhaozunjin/RetinexDIP.

More frequent extreme rainfall events associated with global climate change cause greater challenges for soil conservation. Severe erosion occurs on many unpaved roads since these structures create important water flow paths during heavy storms. The present research aimed to investigate the intensity and influencing factors of unpaved road erosion under varied land use and management conditions (sloping cropland, terraced cropland, forest & grass). The erosion occurred in the watersheds contributing runoff water to roads after the greatest rainfall event recorded in the mountain area of northern China caused by Typhon Lekima. The research was conducted in an agricultural-forest-dominated watershed based on field investigation and UAV-based image analysis. A road erosion level classification standard was given according to the occurrence of rills, ephemeral gullies, and gullies. Significant erosion happened on 67% of the unpaved roads; 42% of them suffered moderate to severe erosion in which ephemeral gullies or gullies developed. The average erosion amount from these roads was 2280.75 t ha-1 and was significantly influenced by the watershed land use type and management. The dominant factor governing unpaved road erosion associated with terraced cropland was vegetation coverage on roads. Drainage area was the most important factor for road erosion in sloping cropland and forest & grass land, and road gradient was also a critical factor. Terraces, and forest & grass in drainage areas significantly reduced unpaved road erosion by 85% and, 47%, respectively, compared to sloping cropland. More integrated measures should be used to prevent unpaved road erosion. The results of this research can be applied to road protection against erosion in heavy storms.

Vehicular Edge Computing (VEC) is a promising paradigm based on the Internet of vehicles to provide computing resources for end users and relieve heavy traffic burden for cellular networks. In this paper, we consider a VEC network with dynamic topologies, unstable connections and unpredictable movements. Vehicles inside can offload computation tasks to available neighboring VEC clusters formed by onboard resources, with the purpose of both minimizing energy consumption of systems and satisfying task latency constraints. For online task scheduling, existing researches either design heuristic algorithms or leverage machine learning, e.g., Deep Reinforcement Learning (DRL). However, these algorithms are not efficient enough because of their low searching efficiency and slow convergence speeds for large-scale networks. Instead, we propose an imitation learning enabled online task scheduling algorithm with near-optimal performance from the initial stage of the system. Specially, an expert can obtain the optimal scheduling policy by solving the formulated optimization problem with a few samples offline. For online learning, we train a learning policy by following the expert's demonstration with an acceptable performance gap in theory. Performance results show that our solution has a significant advantage with more than 50% improvement compared with the benchmark.

Chromium is a harmful contaminant showing mutagenicity and carcinogenicity. Therefore, detection of chromium requires the development of low-cost and high-sensitivity sensors. Herein, blue-fluorescent carbon quantum dots were synthesized by one-step hydrothermal method from alkali-soluble Poria cocos polysaccharide, which is green source, cheap and easy to obtain, and has no pharmacological activity due to low water solubility. These carbon quantum dots exhibit good fluorescence stability, water solubility, anti-interference and low cytotoxicity, and can be specifically combined with the detection of Cr(VI) to form a non-fluorescent complex that causes fluorescence quenching, so they can be used as a label-free nanosensor. High-sensitivity detection of Cr(VI) is achieved through internal filtering and static quenching effects. The fluorescence quenching degree of CDs fluorescent probe showed a good linear relationship with Cr(VI) concentration in the range of 1–100 μM. The linear equation was F0/F = 0.9942 + 0.01472[Cr(VI)] (R2 = 0.9922), and the detection limit can be as low as 0.25 μM (S/N = 3), which has been successfully applied to Cr(VI) detection in actual water samples herein.

A spatiotemporally dynamic therapy (SDT) is proposed as a powerful therapeutic modality that provides spatially dynamic responses of drug-carriers for adapting to the wound microenvironment. Herein, dynamic chitosan-poly (ethylene glycol) (CP) Schiff-base linkages are employed to perform SDT by directly converting a liquid drug Kangfuxin (KFX) into a gel formation. The obtained KFX-CP drug-gel with shape-adaptive property is used to treat a representative oral mucositis (OM) model in a spatiotemporally dynamic manner. The KFX-CP drug-gel creates an instructive microenvironment to regulate signaling biomolecules and endogenous cells behavior, thereby promoting OM healing by the rule of dynamically adjusting shape to fit the irregular OM regions first, and then provides space for tissue regeneration, over KFX potion control and the general hydrogel group of CP hydrogel and KFX-F127. Most interestingly, the regenerated tissue has ordered structure like healthy tissue. Therefore, the SDT provides a new approach for the design of next generation of wound dressing and tissue engineering materials., Graphical abstract Image 1, Highlights • Spatiotemporally dynamic therapy (SDT) is demonstrated via a shape self-adaptive drug-gel. • The KFX-CP drug-gel achieves a drug encapsulation efficiency of 100%. • SDT significantly promotes tissue regeneration with ordered structure. • Spatiotemporally dynamic response plays the key role in tissue re-modelling.

The convergence of Software-Defined Networking (SDN) and Internet of Vehicular (IoV) integrated with fog computing, known as software-defined vehicle network-based fog computing (SDV-F), has recently been established to take advantage of both paradigms and efficiently control the wireless networks. SDV-F tackles numerous problems, such as scalability, load-balancing, energy consumption, and security. It lags, however, in providing a promising approach to enable ultra-reliable and delay-sensitive applications with high vehicle mobility over SDV-F. We propose ARTNet, an AI-based Vehicle-to-Everything (V2X) framework for resource distribution and optimized communication using the SDV-F architecture. ARTNet offers ultra-reliable and low-latency communications, particularly in highly dynamic environments, which is still a challenge in IoV. ARTNet is composed of intelligent agents/controllers, to make decisions intelligently about i) maximizing resource utilization at the fog layer, and ii) minimizing the average end-to-end delay of time-critical IoV applications. Moreover, ARTNet is designed to assign a task to fog nodes based on their states. Our experimental results show that considering a dynamic IoV environment, ARTNet can efficiently distribute the fog layer tasks while minimizing the delay.

Electrocatalytic carbon dioxide reduction (CO2R) presents a promising route to establish zero-emission carbon cycle and store intermittent renewable energy into chemical fuels for steady energy supply. Methanol is an ideal energy carrier as alternative fuels and one of the most important commodity chemicals. Nevertheless, methanol is currently mainly produced from fossil-based syngas, the production of which yields tremendous carbon emission globally. Direct CO2R towards methanol poses great potential to shift the paradigm of methanol production. In this perspective, we focus our discussions on producing methanol from electrochemical CO2R, using metallomacrocyclic molecules as the model catalysts. We discuss the motivation of having methanol as the sole CO2R product, the documented application of metallomacrocyclic catalysts for CO2R, and recent advance in catalyzing CO2 to methanol with cobalt phthalocyanine-based catalysts. We attempt to understand the key factors in determining the activity, selectivity, and stability of electrocatalytic CO2-to-methanol conversion, and to draw mechanistic insights from existing observations. Finally, we identify the challenges hindering methanol electrosynthesis directly from CO2 and some intriguing directions worthy of further investigation and exploration.

In this work, a covalent organic framework (COF), which is constructed by the building blocks of [5,10,15,20-tetrakis(4-aminophenyl)porphinato]copper(II) (CuTAPP) and p-benzaldehyde, is employed to integrate with TiO2 for the purpose of establishing a Z-scheme hybrid. Within the system, isonicotinic acid performs the role of a bridge that connects the two components through a coordination bond. Further photocatalytic application reveals the hybrid framework is able to catalyze CO2 conversion under simulated solar light, resulting in CO production rate of 50.5 μmol g−1 h−1, about 9.9 and 24.5 times that of COF and pristine TiO2, respectively. The ameliorated catalytic performance owes much to the porphyrin block acting as photosensitizer that augments the light absorbance, and the establishment of Z-scheme system between the inorganic and organic components that enhances the separation of the carriers. In addition, the chemical bridge also ensures a steady usage and stable charge delivery in the catalysis. Our study sheds light on the development of versatile approaches to covalently incorporate COFs with inorganic semiconductors.

In this paper, we considered a time-optimal control problem for a new type of linear parameter varying (LPV) system which is obtained through data identification in the process of dealing with actual problems. The addition of non-linear terms is compensation for the method that does not require linear expansion at the equilibrium point. Since the objective function is the terminal time which is an implicit function concerning decision variables, it is a non-standard optimal control problem with uncertain terminal time. To find the global optimal solution to this problem, firstly, the control parameterization method is used to transform it into a nonlinear optimization problem of parameter selection, and then the modifed particle swarm optimization (PSO) algorithm is combined to solve the equivalent nonlinear programming problem. Numerical examples are used to illustrate the effectiveness of the proposed algorithm.

The operational stability of organic (opto)electronic devices largely depends on the intrinsic stability of organic materials on service. For organic light-emitting diode (OLED) materials, a key pa...

While serious stability issues impede the commercialization of perovskite solar cells (PSCs), two-dimensional (2D) perovskites based on fluorinated bulky cations have emerged as more intrinsically stable materials. However, the influence of fluorination degree of the bulky aromatic cation on the performance of resulting PSCs has not been scrutinized. Here, 2D perovskites (FxPEA)2PbI4 (x = 1, 2, 3, 5) are grown in situ on the surface of the three-dimensional (3D) perovskite and demonstrate effective passivation of the surface defects of 3D perovskite. The power conversion efficiency (PCE) of the optimized devices were boosted from 20.75% for the control device to 21.09%, 22.06%, 22.74% and 21.86% for 2D/3D devices treated with 4-fluorophenethylamine iodide, 3,5-difluorophenylethylamine iodide, 2,4,5-trifluoroethylphenylethylamine iodide, and 1,2,3,4,5-pentafluorophenylethylamine iodide, respectively. We firstly reported two unexplored RP-type layered perovskites with F2PEAI and F3PEAI as bulky cations. The combined experimental and theoretical analysis revealed the reasons behind the various morphology, device performances, dynamic behavior, and humidity stability. The best performing F5PEAI-treated device retaining 95.0% of its initial PCE under ambient atmosphere (with RH of 60% ± 5%) without encapsulation for 300 h storage. This work provides useful guidance for selecting fluorinated bulky cations with different molecular electronic properties, which will play an essential role in further improving the performance/stability of PSCs for the sake of further commercialization.

A visible-light-induced chemoselective reactions of quinoxalin-2(1H)-ones with alkylboronic acids in the presence of air (O2) and N2 atmosphere was developed under transition-metal free conditions, providing 3-alkylquinoxalin-2(1H)-ones and 3,4-dihydroquinoxalin-2(1H)-ones, respectively. The overall strategy accommodates a broad scope of substituted quinoxalin-2(1H)-ones and alkylboronic acids with good to excellent product yields.

Hierarchical microstructures of electrode materials for supercapacitors have attracted strong attention owing to the enlarged surface area, fast charge transportation and enhanced volume expansion. All these advantages of hierarchical structures are available for facilitating the electrochemical performances and stability of electrode materials for advanced supercapacitors. However, few reports focused on the development of transition metal-based homogeneous 2D/1D hierarchical structures use the same material. Herein, the ZnCo-LDH with 3D hierarchical microstructure was self-assembled with 1D nanoneedles and 2D nanosheets that in situ synthesized on Ni foam through a simple and effective strategy. The optimal sample (ZnCo-LDH-2) exhibited the ultra-high specific capacitance of 3871.2 F g −1 at 1 A g −1 and excellent cycle life span (capacitance retention of 87.5% after 6000 cycles at 5 A g−1). Particularly, the current density was as high as 100 A g−1, the specific capacitance of ZnCo-LDH-2 still remained 1526 F g−1. Moreover, the symmetric supercapacitor (SSC) fabricated with ZnCo-LDH-2 showed the maximum energy density of 40.3 Wh kg−1 and power density of 15.08 kW kg−1.

Neuromorphic event-based dynamic vision sensors (DVS) have much faster sampling rates and a higher dynamic range than frame-based imagers. However, they are sensitive to background activity (BA) events that are unwanted. We propose HashHeat, a hashing-based spatiotemporal BA filter for DVS. It is the first spatiotemporal filter that doesn't scale with the DVS output size and doesn't store the 32-bits timestamps. We not only give the visual denoising effect of the filter but also use two metrics for quantitatively analyzing the filter's global performance and local performance respectively, where we introduce a novel metric for evaluating global performance. The experimental results show that HashHeat achieves similar global performance as baseline filters, but increases the signal to noise ratio by about 1.5x to nearly 5x compared with other baseline filters concerning the local performance. The hardware implementation enables HashHeat to output a labeled event every 10ns and meets the real-time requirement. And it can reduce the storage cost by 128x to 256x compared with baseline filters.

Many large-scale and complex structural components are applied in the aeronautics and automobile industries. However, the repeated alternating or cyclic loads in service tend to cause unexpected fatigue fracture. Therefore, developing real-time and visible monitoring methods for fatigue crack initiation and propagation is critically important for the structural safety. This paper proposes a machine learning-based fatigue crack growth detection method that combines computer vision and machine leaning. In our model, computer vision is used for data creation, and the machine learning model is used for crack detection, then computer vision is used for marking and analyzing the crack growth path and length. We apply seven models for the crack classification and find that the decision tree is the best model in this research. The experimental results prove the effectiveness of our method and the crack length measurement accuracy achieved is 0.6 ​mm. Furthermore, the slight machine learning models help us realize the real-time and visible fatigue crack detection.

Although TiO2 is a wide band gap semiconductor, it demonstrates photodegradation activity under visible light irradiation after dye sensitization. Compared with esterification between the surface hydroxyl group of TiO2 and the carboxylic group of dyes, electrostatic interaction between TiO2 and dye shows better photosensitized performance. In this work, Bi/Al co-doping anatase titania (Ti1-xBi2x/3Al2x/3O2 (0 ≤ x ≤ 0.3)) is designed to enhance the electrostatic interaction. The effect of Al ions is to enhance the solubility of Bi3+ into titania nanocrystals. A new band gap is generated after high concentration of Bi element doping, which not only promotes the absorption of visible light, but also improves the utilization of photogenerated carriers. Based on the results of transmission electron microscopy, light absorption, photodegradation activity and density functional theory calculations, it is found that bismuth dopant is the electron capture site. It first accumulates electrons through photocatalytic degradation reaction to enhance electrostatic adsorption between the catalyst and the positively charged dye molecules, and finally realizes high-efficiency photosensitization degradation of Rhodamine B. In addition, the sensitized titania has excellent photodegradation recyclability.

In alumina-based catalysts the properties of the alumina support directly influence the structure (size, morphology, orientation, etc.) of the resulting active phase and ultimately determine the performance of the final catalysts. This paper presents a facile and efficient method for γ-Al2O3 to simultaneously expose {111} facets with strong acidity and {110} facets with moderate basicity. It is shown that the metal oxides NiOx and MoOx can be selectively deposited and subsequently sulfided on the respective facets to form a NiS2-MoS2/Al2O3 catalyst with substantially enhanced activity for thio-etherification reaction. By cooperative action of the active sites on the newly exposed facets, the catalyst is shown to have superior activity over a conventional NiMoS/Al2O3 catalyst in the thio-etherification reaction between ethanethiol and isoamylene (2-methyl-2-butene) to yield ethylamyl thioethers. Our findings show that tuning the physicochemical properties via crystal facet engineering and inducing cooperative catalysis via preferential deposition of active metals open a novel avenue for the rational design and synthesis of high performance catalysts.

The influence of different precipitate-dislocation interactions, namely dislocation shearing and bypassing mechanisms, on PLC bands and the resultant surface roughness in AlMgScZr alloy was investigated. Three-dimensional surface roughness was quantitatively measured by confocal microscopy. We find that the introduction of shearable precipitates increases the stress amplitude, decreases the PLC bands number and surface roughness. However, the stress amplitude decreases, the PLC bands number and surface roughness increase with shearable precipitates turning to nonshearable precipitates. By analyzing the precipitation strengthening mechanisms quantitatively, the influence of precipitates on PLC bands and the resultant surface roughness was explored. Furthermore, our study demonstrates that the shearable precipitates can decrease the surface roughness by decreasing the number of PLC bands, which is instructive for designing structural materials with desirable mechanical property and surface quality.

Non-noble-metal-based electrocatalysts with superior oxygen reduction reaction (ORR) activity to platinum (Pt) are highly desirable but their fabrications are challenging and thus impeding their applications in metal-air batteries and fuel cells. Here, we report a facile molten salt assisted two-step pyrolysis strategy to construct carbon nanosheets matrix with uniformly dispersed Fe3N/Fe nanoparticles and abundant nitrogen-coordinated Fe single atom moieties (Fe@FeSA-N-C). Thermal exfoliation and etching effect of molten salt contribute to the formation of carbon nanosheets with high porosity, large surface area and abundant uniformly immobilized active sites. Aberration-corrected high-angle annular dark-field scanning transmission electron microscopy (HAADF-STEM) image, X-ray absorption fine spectroscopy, and X-ray photoelectron spectroscopy indicate the generation of Fe (mainly Fe3N/Fe) and FeSA-N-C moieties, which account for the catalytic activity for ORR. Further study on modulating the crystal structure and composition of Fe3N/Fe nanoparticles reveals that proper chemical environment of Fe in Fe3N/Fe notably optimizes the ORR activity. Consequently, the presence of abundant FeSA-N-C moieties, and potential synergies of Fe3N/Fe nanoparticles and carbon shells, markedly promote the reaction kinetics. The as-developed Fe@FeSA-N-C-900 electrocatalyst displays superior ORR performance with a half-wave potential (E1/2) of 0.83 V versus reversible hydrogen electrode (RHE) and a diffusion limited current density of 5.6 mA cm−2. In addition, a rechargeable Zn-air battery device assembled by the Fe@FeSA-N-C-900 possesses remarkably stable performance with a small voltage gap without obvious voltage loss after 500 h of operation. The facile synthesis strategy for the high-performance composites represents another viable avenue to stable and low-cost electrocatalysts for ORR catalysis.

Corrosion inhibitor microcapsules were prepared by centrifugation-coating method and the influence factors on the morphology of microcapsules were discussed. Process technology was optimized by orthogonal design method, with the rotational speed of turntable at 500 r/min, the fan speed at 400 r/min, the speed of peristaltic pump at 15 r/min and the jet pressure at 0.20 MPa. Analysis on the surface morphology, thermal stability and mechanical properties of microcapsules revealed that the obtained microcapsules had high drug loading rate and good mechanical strength, and higher weight gain rate of wall material can obtain thicker wall. Their release rates measured in the simulated concrete pore solution showed that microcapsules with higher weight gain rates had slower release rate, polymethyl methacrylate as wall material released faster than ethyl cellulose.

In the past few years, the prediction models have shown remarkable performance in most biological correlation prediction tasks. These tasks traditionally use a fixed dataset, and the model, once trained, is deployed as is. These models often encounter training issues such as sensitivity to hyperparameter tuning and "catastrophic forgetting" when adding new data. However, with the development of biomedicine and the accumulation of biological data, new predictive models are required to face the challenge of adapting to change. To this end, we propose a computational approach based on Broad learning system (BLS) to predict potential disease-associated miRNAs that retain the ability to distinguish prior training associations when new data need to be adapted. In particular, we are introducing incremental learning to the field of biological association prediction for the first time and proposed a new method for quantifying sequence similarity. In the performance evaluation, the AUC in the 5-fold cross-validation was 0.9400 +/- 0.0041. To better assess the effectiveness of MISSIM, we compared it with various classifiers and former prediction models. Its performance is superior to the previous method. Besides, the case study on identifying miRNAs associated with breast neoplasms, lung neoplasms and esophageal neoplasms show that 34, 36 and 35 out of the top 40 associations predicted by MISSIM are confirmed by recent biomedical resources. These results provide ample convincing evidence of this approach have potential value and prospect in promoting biomedical research productivity.

The kinematics of the heald frame of a rotary dobby with two different modulator types are analyzed and compared. Kinematic mathematical models of the modulator main shaft, cam unit, and heald frame driven by the rotary dobby with a cam-slider modulator and a cam-link modulator were constructed based on two different cam contours derived from measured points on the conjugate cams of the two modulators. The motion characteristics of the two modulators and a null modulator, the cam unit, and the motion transmission unit are analyzed. The purpose of the present study was to establish the kinematic models, investigate the motion characteristics, and analyze their differences. At the same time, a calculation method for each motion transmission process was established and numerical models were developed. The results demonstrated that the two different modulators produce almost the same heald frame motion characteristics. Despite that both modulator types can be adapted to the requirements of a loom, the cam-link modulator can produce a more stable and reliable motion.

Understanding seasonal soil erosion and deposition rates and their spatial distribution along sloping farmlands are necessary for erosion prediction technology and implementing effective soil conservation practices. To date seasonal change of soil erosion and soil redistribution on long gentle hillslopes are not fully quantified due to the variable erosive forces in different seasons. A multi-tracer method using rare earth elements (REE) was employed to discriminate seasonal changes of soil erosion and its spatial distribution on a sloping farmland driven by snowmelt runoff, wind force and rainfall-runoff. A long-slope runoff plot with 5 m wide and 320 m long located in the typical Mollisol region of China was divided into eight segments, each of which was 40 m long and tagged with one of eight REE oxides. The spot method of a partial-area tagging scheme was employed and a grid-based layout was used for REE application. Results showed that annual soil erosion rate was 3251.0 t km−2 for the whole runoff plot, in which snowmelt runoff erosion contributed 537.3 t km−2, wind erosion 363.1 t km−2 and rainfall-runoff erosion 2350.6 t km−2. Surface runoff is the main external erosive force of hillslope soil erosion, accounting for 88.7% of the total annual soil loss. Furthermore, for the eight slope segments of the 320-m long hillslope, the sediment transport ratios of each slope segment caused by snowmelt runoff and rainfall-runoff erosion were more than 23.5% and 34.7%, respectively. The results will enrich the understanding of seasonal soil erosion on long hillslopes.

An omnidirectional wireless local area network (WLAN) loop-slot antenna with common-mode current (CMC) suppression is proposed in this communication. First, based on the analysis of the equivalent model of odd–even mode, the feeding coaxial line is led out at the voltage-zero point of the loop-slot element, which suppresses the CMC over the full band. Second, through the theory of array, three loop-slot elements form an array to obtain omnidirectional radiation. Finally, the physical size is reduced by loading Stubs T in the slot. The roundness of the radiation pattern is modified by the Stubs T and the chamfering edge of the antenna. A prototype of the proposed antenna was fabricated and measured. The measured results show that the antenna achieves $\vert \text{S}_{11}\vert dB in bands of 2.15–2.65 and 4.85–5.92 GHz. In addition, the roundness is 0.95 dB at 2.4 GHz, 1.84 dB at 5.2 GHz, and 2.43 dB at 5.8 GHz. The antenna effectively suppresses the CMC to keep a favorable roundness over a wide band, showing its potential in WLAN communication systems.

This article is concerned with the problem of event-triggered (ET) fault detection (FD) observer design for Takagi–Sugeno (T–S) fuzzy systems. The nonlinear systems considered are presented as T–S models, and the system outputs are selected as the premise variables (PV) of T–S models. New types of observers, namely, switched fuzzy FD observers, are designed, which are switched at different triggering instants based on the online asynchronous premise reconstruction (APR) method. By employing new triggering instants-dependent Lyapunov functions (LFs) established via the idea of event interval partitioning, it is proved that the proposed FD observers can guarantee the stability and desired performance of the systems. Finally, a simulation example is applied to show that the proposed FD scheme can achieve better FD results than the existing design methods.

Diamide compounds such as chlorantraniliprole, a famous anthranilic diamide insecticide targeting the insect ryanodine receptor (RyR), have received continuous attention in pesticide research during the past 15 years owing to their excellent insecticidal potentials. With the aim of discovering new heterocyclic pesticides used for crop protection, based on the structural information of compound M from the reported pharmacophore-based virtual screening for RyR insecticides and diamide compound, a series of new heterocyclic mono-, di-, and tri-amide derivatives containing piperazine moiety have been synthesized in this paper. The new compounds were identified and confirmed by melting point, 1H NMR, 13C NMR and HRMS. Compound M was firstly validated for insecticidal activities, and the new synthesized compounds were all made comprehensive insecticidal evaluations against diamondback moth and oriental armyworm. The bioassay results showed that some of the compounds exhibit favorable insecticidal potentials, particularly some novel piperazine-containing heterocyclic mono-/di-/tri-amide derivatives such as 8g, 14a, 15a, 15g, 15i, 15j, 15k, 15l, and 15m could be used as new insecticidal leading structures for further study (e.g., towards diamondback moth, 15i-15m LC50: 0.0022−0.0081 mg/L). The structure-activity relationships of the compounds discussed in detail provide useful guidance for further design and development of new insecticides.

It is prominent important for managers to assess the personal risk of mental patients. The evaluation process refers to numerous indexes, and the evaluation values are general portrayed by uncertainty information. In order to conveniently model the complicated uncertainty information in realistic decision making, interval-valued complex Pythagorean fuzzy set is proposed. Firstly, with the aid of Einstein t-norm and t-conorm, four fundamental operations for interval-valued complex Pythagorean fuzzy number (IVCPFN) are constructed along with some operational properties. Subsequently, according to these proposed operations, the weighted average and weighted geometric forms of aggregation operators are initiated for fusing IVCPFNs, and their anticipated properties are also examined. In addition, a multiple attribute decision making issue is examined under the framework of IVCPFNs when employing the novel suggested operators. Ultimately, an example regarding the assessment on personal risk of mental patients is provided to reveal the practicability of the designed approach, and the attractiveness of our results is further found through comparing with other extant approaches.The main novelty of the coined approach is that it not only can preserve the original assessment information adequately by utilizing the IVCPFNs, but also can aggregate IVCPFNs effectively.

Mandarin (Citrus reticulata L.) essential oil (MEO) reportedly displays excellent antimicrobial properties. In this study, MEO was loaded into chitosan nanoparticles (CSNPs). The characteristics, antibacterial properties and benefit in pork preservation of MEO-CSNPs were evaluated. The MEO-CSNPs displayed an excellent encapsulation efficiency (EE) (67.32%–82.35%), the particle size values of 131.3 nm–161.9 nm, and the absolute zeta potential values above 30 mV. Fourier transform infrared spectroscopy (FTIR), X-ray diffraction (XRD) analysis, and thermogravimetric analysis (TGA) revealed that the MEO was incorporated into CSNPs without requiring a chemical reaction, the antibacterial activity of the MEO remained. Furthermore, the damage of MEO-chitosan nanoemulsions (MEO-CSs) to the cell membranes of Staphylococcus aureus (S. aureus) and Escherichia coli (E. coli) was confirmed by the change of bacterial cell morphology. The anti-biofilm assays verified that the MEO-CSs substantially inhibited biofilm formation and destroyed the mature biofilms. MEO-CSs were also applied to pork, proving a great potential for pork preservation. This study provides a potential approach for developing and utilizing MEO-CSs as natural antimicrobial agents in the food industry.

In this study, an ultra-broadband orbital angular momentum (OAM) mode generator based on a phase-modulated helical long period fiber grating (HLPG) working at highradial order cladding mode is firstly proposed and numerically demonstrated. The proposed phase-only modulation method can considerably facilitate the HLPG’s fabrication by eliminating the grating’s apodization, which is extremely difficult to realize in the screw-type grating. Meanwhile, the proposed method provides more flexibilities in the design of HLPGs. As a typical example, a first-order OAM (OAM −1) mode generator with a bandwidth of ~63 nm, a conversion efficiency of ~100%, and purity of ~ 91% has been numerically demonstrated. Moreover, an OAM −1 mode generator with a bandwidth of ~469 nm, a conversion efficiency of ~100%, and purity of ~ 91% has been successfully obtained, covering all bands of fiber communication, i.e., o riginal- (O-), extended- (E-), short- (S-), conventional- (C-), long- (L-), and ultralong- (U-) wavelength bands, which, based on our knowledge, are the best results reported to date among those of the HLPG-based OAM mode generators. The proposed component may find potential applications in the OAM mode division multiplexing (MDM) system combined with the wavelength division multiplexing technique.

Regulating the coordination environment of metal–Nx species by replacing N with low electronegativity atoms is an approach of tuning the electrocatalytic performance of metal-based sites. However, such effects on the enhancement of photocatalytic H2 evolution over semiconductors are not discussed yet. Herein, we designed and prepared Co-based cocatalysts with controlled coordination environment via calcination Co/ZIF-8 loaded with triphenylphosphine followed by a sulfurization treatment. It was then used as cocatalyst to modify CdS. The effects of the coordination environment of Co atoms on the H2 evolution activity of CdS were discussed. The obtained Co was co-stabilized by N, P, and S atoms and embedded in graphitic carbon (denoted as Co-NxPS/C). Experimental results indicated that the Co-NxPS/C exhibited high activity in enhancing H2 evolution of CdS with a value of 1260 μmol after 5 h irradiation. The simultaneous replacement of N with P and S atoms in N-stabilized Co embedded in carbon could enhance light harvesting, accelerate the transfer of photogenerated electrons from CdS to carbon with increased electrons accumulation ability and conductivity, improve charge separation efficiency, and enhance proton reduction kinetics. It is believed that the results of this study could promote the development of other high performance MOF-derived atomically dispersed cocatalysts to increase photocatalytic H2 evolution.

The scalar triangulation and ranging (STAR) method is a magnetic detection method based on magnetic gradient tensor invariants, which has a wide application prospect. Due to the asphericity parameter, there are distance error and direction error in the STAR method. There have been studies to compensate the direction error, but the distance error has not been compensated. This article proposes a new STAR method (NSM) that uses an iterative method to compensate both the direction error and the distance error. Considering the convenience and feasibility of the operation, previous researchers analyzed the detection accuracy of the magnetic detection method by the detection error of the magnetic target on a latitude trajectory (latitude line in the spherical coordinate system of magnetic detection). But different latitude trajectories will lead to different analysis results. In order to analyze the influence of motion trajectory on the detection error, the omnidirectional magnetic detection model (OMDM) is established. The omnidirectional error expectation is used to more accurately measure the detection accuracy of the magnetic detection method. Based on the OMDM, the warp trajectory (warp line in the spherical coordinate system of magnetic detection) that accurately measures the detection accuracy is found. Simulation results show that NSM reduces the localization error of the STAR method, Lv-STAR method, and Wang-STAR method by 87.5%, 58.8%, and 12.5%, respectively. Both simulation and experimental results show that when comparing and analyzing the detection accuracy of the magnetic detection method, the results obtained by a latitude trajectory are one-sided, but a warp trajectory is both operable and accurate.

In this paper, based on a bidirectional parallel multi-branch feature pyramid network (BPMFPN), a novel one-stage object detector called BPMFPN Det is proposed for real-time detection of ground multi-scale targets by swarm unmanned aerial vehicles (UAVs). First, the bidirectional parallel multi-branch convolution modules are used to construct the feature pyramid to enhance the feature expression abilities of different scale feature layers. Next, the feature pyramid is integrated into the single-stage object detection framework to ensure real-time performance. In order to validate the effectiveness of the proposed algorithm, experiments are conducted on four datasets. For the PASCAL VOC dataset, the proposed algorithm achieves the mean average precision (mAP) of 85.4 on the VOC 2007 test set. With regard to the detection in optical remote sensing (DIOR) dataset, the proposed algorithm achieves 73.9 mAP. For vehicle detection in aerial imagery (VEDAI) dataset, the detection accuracy of small land vehicle (slv) targets reaches 97.4 mAP. For unmanned aerial vehicle detection and tracking (UAVDT) dataset, the proposed BPMFPN Det achieves the mAP of 48.75. Compared with the previous state-of-the-art methods, the results obtained by the proposed algorithm are more competitive. The experimental results demonstrate that the proposed algorithm can effectively solve the problem of real-time detection of ground multi-scale targets in aerial images of swarm UAVs.

During the working process of high-speed multiaxis machine tools, inertial forces can cause vibration and deformation of mechanical structure, which lead to the dynamic error of tool center point (TCP) relative to worktable and can adversely affect the machining performance. Considering the varying feed positions and accelerations during machining, a parameter-varying multi-rigid-body dynamic model of a 3-axis gantry machine tool is proposed. This model represents the position dependent structural dynamics and inertial forces, which can simulate the dynamic error of TCP relative to worktable within the entire workspace. The results show that the dynamic error in one direction is affected by the feed motions of multiple feed axes. The magnitudes of the dynamic error significantly vary with the position of Z-axis. And the dynamic errors in Y- and Z-direction show different varying trends. Then the theoretical model is used to discuss the dynamic error and position dependency. The expressions of TCP dynamic response and inertial forces reveal the reason why the dynamic errors in Y- and Z-direction show different varying trends.

The ground pressure distributes significant variation in underground mining near fault. Fault reactivation is an important factor to induce the rock burst. Therefore, characterizing geological settings in mining areas by the geological information can improve the accuracy of simulation. To investigate the characteristic of mining stress evolution and reactivation of the F16 reverse fault during the retreat Mining-Induced s in Yima coalfield, a three-dimensional digital elevation model based on GIS platform was applied. The 3D geological model includes three working faces, and F16 fault was constructed by AutoCAD software. Then, the 3D geological model was imported into the FLAC3D code to simulate the potential of mining-induced fault reactivation. The simulation results illustrate that the footwall of F16 fault is a high stress concentration area. Affected by F16 fault and the huge thick gravel rock in the roof, the coal seam near the fault accumulates a large amount of elastic strain energy, which increases the potential of rock burst hazards in the process of mining.

Polymer derived silicon oxycarbide ceramic materials and silicon carbide whiskers reinforced ceramic composite are prepared through digital light processing (DLP) 3D printing technology in the present work. A new type of UV-curable preceramic polymer is firstly synthesized and then two types of photopolymer resins with and without SiC whiskers as reinforcement are prepared. Due to the high curing rate and good fluidity of the resins, they are applied in DLP 3D printing and various 3D objects with complicated structures and high printing resolution have been printed. The derived ceramic materials show amorphous microstructure and there is no apparent porosity and cracking throughout the whole sample surface of the ceramic materials and the SiC whiskers are uniformly embedded in the ceramic matrix and remain intact and unaffected during the pyrolysis process. The SiC whiskers reduced the shrinkage and mass loss. More importantly, it significantly improves the mechanical performance of the derived ceramic materials in which the compressive strength increases from 77.5 ± 10.2 MPa to 98.4 ± 12.3 MPa. Benefiting from the easiness of the fabrication, high printing resolution and excellent mechanical performance, the derived ceramic materials have great potential applications in various areas.

Convergence is always a major concern for many-objective optimization problems. Over the past few decades, various methods have been designed for measuring the convergence. However, according to our mathematical and empirical analyses, most of these methods are more focused on the convergence, and may neglect the exploration of boundary solutions, resulting in the incomplete Pareto fronts and the poor extent of spread achieved among the obtained non-dominated solutions. Regarding this issue, this paper proposes a Many-Objective Evolutionary Algorithm with Adaptive Reference Vector (MaOEA-ARV). In MaOEA-ARV, an adaptive reference vector strategy is designed to dynamically adjust the reference vectors according to the current distribution of candidate solutions for ensuring the spread and convergence simultaneously. Additionally, a hierarchical clustering strategy is employed to adaptively partition candidate solutions into multiple clusters for the diversity of candidate solutions. Experimental results on DTLZ, BT, ZDT and WFG test suites with up to 12 objectives demonstrate the effectiveness of MaOEA-ARV.

The highly thermal stability precipitates strengthen creep resistance for alloys, which require precipitates to resist coarsen at elevated temperature. In the Mg-RE-Zn (Ag) series alloys, the key strengthening phase - γ’’ phase, remains its single-unit-cell height throughout aging process. The origin of such extremely thermal stability of γ’’ phase is still unclear. By using the first-principles calculations, it is found that the formation of γ’’ phase introduces compressive strain to its surrounding α-Mg lattice and simultaneously alter charge distribution of α-Mg lattice near the α-Mg/γ’’ hetero-interface. These two variations over α-Mg lattice lead to lower vacancy formation energies and migration energy barriers for solute atoms near the α-Mg/γ’’ hetero-interface in comparison with that of bulk condition. Consequently, the variations facilitate rapid diffusion of solute atoms near the γ’’ phase and promote nucleation rate of other γ’’ plates around it. On the basis of ledge-thickening model, the origin of nucleation/growth of γ’’ phase with single-unit-cell height was explained. Thermodynamically and kinetically, the solute atoms are not apt to migrate into the nearest basal plane adjacent to the γ’’ phase. Moreover, the lower aging temperature (∼200 °C) and almost completely coherent α-Mg/γ’’ hetero-interface lead to the ledges are extremely hard to nucleate on the hetero-interface. Therefore, γ’’ plates maintain single-unit-cell height throughout aging process.

There has been a great challenge in developing highly efficient electromagnetic microwave absorbers with strong absorbility, wide effective absorbing bandwidth and lightweight to solve the increasingly severe electromagnetic pollution. Here, we unveil the great potential of highly ordered mesoporous carbon coated graphene composites in the application of microwave absorption. The ordered mesoporous carbon composites possess well-defined spherical mesopores that are uniformly distributed on graphene surfaces and with uniform diameters of ∼20 nm (graphene@mesoporous carbon, abbreviated as G/MC). This renders the nanocomposites with high BET surface areas of up to 316 m2 g−1. Impressively, the G/MC nanosheets carbonized at 900 °C (G/MC-900) exhibit an outstanding microwave absorbility, with a maximum reflection loss (RL) of −66.1 dB and an exceptionally broad effective absorbing bandwidth (EAB) of 8.2 GHz (RL

In this work, we propose a lattice Boltzmann method to model the electrohydrodynamic (EHD) conduction phenomenon in dielectric liquids. To incorporate the effect of the source term appeared in the charge conservation equations, some unified force terms that can be implemented locally are delicately designed in the charge evolution equations. We further validate the present model against some analytical solutions as well as experimental data for several problems, and a good agreement is obtained. Then it is used to study the fluid circulation in a square cavity generated by EHD conduction phenomenon, and the numerical results indicate that the geometric scale and the difference in the mobilities of the positive and negative charges could play an important role in studying EHD conduction phenomenon. Further, to analyze the characteristics of the EHD conduction mechanisms in the ohmic and saturation regimes, the influences of the conduction number W 0 as well as the parameter β , which is related to Onsager effect, are also investigated, and the numerical results fit well with previous work.

Human activity recognition using WiFi signals is widespread for smart-environment sensing domain in recent years. Existing researches use learning-based methods to obtain several features of activity data and then recognize human activities. As we know, propagation characteristics of WiFi signals are different for individuals under different place conditions even in the same environment. In this paper, we focus on how to weaken the accuracy differences among individuals on activity recognition and improve the robustness in one indoor environment. Based on this, we design a novel deep learning model called LCED which consists of one LSTM-based Encoder, features image presentation, and one CNN-based Decoder to weaken the accuracy differences among individuals on activity recognition. We first use a low-pass filter to remove high-frequency noise data in time-sequence signal data and design variance-based window method to determine the start and the end of time-sequence signal data corresponding to an activity. After that, we utilize the proposed LCED model to learn informative features space of activity data and improve the accuracy of sixteen activities. Experimental results show that the average accuracy of sixteen activities is high 95 % and the accuracy differences among individuals on activity recognition averagely decreases by 3 % .

Traditionally, the energy management objectives of a fuel cell–battery hybrid electric vehicle (FCBHEV) powertrain system is achieved by using the centralized control strategy, which suffers from poor flexibility, scalability, and reliability. What is worse, the system stability can hardly be guaranteed because of the serious interactions between the fuel cell (FC) as well as the battery converters and motor drive loads. In this article, a decentralized energy management strategy (EMS) based on a composite control scheme that combines mixed droop control and disturbance-observer-based control is proposed to achieve the dynamic load power allocation, extend the service life, improve the energy efficiency, enhance the robustness (i.e., realize the disturbance and uncertainty rejection), and guarantee the global stability of the FCBHEV powertrain system in a decentralized way simultaneously. First, the architecture of an FCBHEV powertrain system is described in detail. Then, analyses on the design and operating principle of the proposed EMS are deeply studied. Finally, a hardware-in-the-loop (HIL) real-time simulation system of a 50 kW FCBHEV powertrain system is established using the Typhoon HIL 602 device. The HIL simulation results verified the correctness of the theoretical analysis and effectiveness of the proposed EMS.

Tunable mid-infrared emission is highly applicable in the fields of molecular spectroscopy and atmospheric monitoring. Recent studies have demonstrated that external electric fields can induce non-zero optical bandgaps of up to 250 meV in graphene materials, thereby paving the way for mid-infrared emissions (3–14 μm) to be realized. Graphene foam (GF) possesses a unique highly porous microstructure, which allows intense reflection or scattering of incident photons and gives rise to efficient interband optical absorption. Enhanced electron–phonon coupling enables free electrons in the conduction band to relax non-radiatively to the 1s-state. Mid-infrared emission is generated owing to electron–hole recombination. In this work, we demonstrate the induction of widely tunable (2.9–8.6 μm) mid-infrared emission in GF by modulated visible laser diode (LD) light with modulation frequencies ranging from 1.5 to 0.5 kHz. The peak emission wavelength correlates with the modulation frequency and is independent of the wavelength of LD light of visible range, whereas the emission intensity is related to the alternating intensity of the LD. Our findings indicate that GF is not only a novel mid-infrared emitting material, but one that promises bandgap flexibility for widely tunable mid-infrared sources.

Exploring highly efficient catalyst is the key for integration of photocatalysis and peroxymonosulfate (PMS) activation. Herein, WC/Bi2WO6 nanocomposites were judiciously constructed by anchoring the non-noble metal plasmonic WC nanocrystals onto the surface of ultrathin Bi2WO6 nanosheets to activate PMS for efficient photodegradation of bisphenol A (BPA). The prepared WC/Bi2WO6 nanocomposites possess abundant heterojunction interface, resulting in excellent photoabsorption capability, interfacial charge transfer dynamics, and charge carrier concentration. As expected, WC/Bi2WO6 nanocomposites exhibit superior catalytic activity and stability in PMS activation toward BPA degradation with removal efficiency of 97.4%, which is higher than that of Bi2WO6. The content of WC nanocrystals are critical to tune the catalytic activity of the nanocomposites. The intermediates and degradation pathways were determined through LC-MS/MS analysis. Finally, the reaction mechanism was systematically elucidated based on the active species detection, charge transfer dynamics, and band structures. The desirable photocatalytic performances can be ascribed to the constructional heterojunction between the WC nanocrystals and Bi2WO6 nanosheets, which can efficiently enhance the photoabsorption capability, heighten the interfacial charge transfer ability, and ameliorate the charge carrier concentration. This study provides some insights to construct highly efficient photocatalyst modification with non-noble metal based plasmonic materials for environmental remediation.

Lithium metal is one of the most promising anode materials for next-generation electrochemical energy storage due to low electrochemical potential and high specific capacity. However, large volume change and uncontrollable formation of lithium dendrite during cycling severely hinder the practical application of rechargeable Li metal batteries. Herein, we report a hierarchically porous Cu covered with lithiophilic CuxO (HPCu-CuxO) via femtosecond laser strategy in about 2 min as current collector for high-performance Li metal batteries. With precisely tunable pore volume and depth as well as lithiophilic CuxO interphase, the HPCu-CuxO not only guides homogeneous Li nucleation, resulting in a smooth and dendrite-free lithium surface, but also provides space to alleviate the volume expansion of Li metal anode, achieving excellent structure stability. Consequently, highly stable Coulombic efficiency and ultra-low overpotential of 15 mV even up to 1000 h were achieved at the current density of 1 mA cm−2. Moreover, the resultant Li@HPCu-CuxO//LiFePO4 full battery delivered outstanding cycle stability and rate capability. These results offer a pathway toward high-energy-density and safe rechargeable Li metal batteries.

Conjugated polymers have attracted considerable attention for thermoelectric applications in recent years due to their plentiful resources, diverse structures, mechanical flexibility, and low thermal conductivity. Herein, we demonstrate a new strategy of modulating charge carrier concentration of chemical-doped polymer films by modifying the substrate with self-assembled monolayers (SAMs). The SAM with a trifluoromethyl terminal group is found to accumulate holes in the polymer thin films, while the SAM with an amino terminal group tends to donate electrons to the polymer films. Thermoelectric thin films of conjugated donor-acceptor copolymer exhibit high power factors of 55.6-61.0 μW m-1 K-2 on SAMs with polar terminal groups. These power factors are 49% higher than that on the SAM with the nonpolar terminal group and 3 times higher than that on pristine substrate. The high power factor is ascribed to the modulated charge carrier concentration and improved charge carrier mobility as induced by SAMs.

The high-valence metal catalysts show extraordinary talent in various electrochemical reactions. However, there is no facile method to synthesize high-valence noble metal-based materials. Herein, we synthesized the different high valence noble metal M-incorporated PdCu nanoparticles (M = Ir, Ru, Rh) by the assistant of Fe3+ and exhibit excellent performance for water electrolysis. In 0.1 M KOH, the OER and HER mass activities of Ir16-PdCu/C were 50.5 and 16.5 times as much as PdCu/C, and achieved a current density of 10 mA cm-2 at 1.63 V when worked for overall water splitting. DFT calculation revealed that the incorporating of high valence Ir could optimize the binding energy of the intermediate products, and promote the evolution of oxygen and hydrogen. Ex situ XPS shows that the huge amount of oxidized Ir (V) formed in OER could promote the formation of O-O bonds.

Eye blink detection plays a key role in many real-life applications such as Human-Computer Interaction (HCI), drowsy driving prevention and eye disease detection. Although traditional camera-based techniques are promising, multiple issues hinder their wide adoption including the privacy concern, strict lighting condition and line-of-sight (LoS) requirements. On the other hand, wireless sensing without a need for dedicated sensors gains a tremendous amount of attention in recent years. Among the wireless signals utilized for sensing, acoustic signals show a unique potential for fine-grained sensing owing to their low propagation speed in the air. Another trend favoring acoustic sensing is the wide availability of speakers and microphones in commodity devices. Promising progress has been achieved in fine-grained human motion sensing such as breathing using acoustic signals. However, it is still very challenging to employ acoustic signals for eye blink detection due to the unique characteristics of eye blink (i.e., subtle, sparse and aperiodic) and severe interference (i.e., from the human target himself and surrounding objects). We find that even the very subtle involuntary head movement induced by breathing can severely interfere with eye blink detection. In this work, for the first time, we propose a system called BlinkListener to sense the subtle eye blink motion using acoustic signals in a contact-free manner. We first quantitatively model the relationship between signal variation and the subtle movements caused by eye blink and interference. Then, we propose a novel method that exploits the "harmful" interference to maximize the subtle signal variation induced by eye blinks. We implement BlinkListener on both a research-purpose platform (Bela) and a commodity smartphone (iPhone 5c). Experiment results show that BlinkListener can achieve robust performance with a median detection accuracy of 95%. Our system can achieve high accuracies when the smartphone is held in hand, the target wears glasses/sunglasses and in the presence of strong interference with people moving around.

Gesture recognition on the back surface of mobile phone, not limited to the touch screen, is an enabling Human-Computer Interaction (HCI) mechanism which enriches the user interaction experiences. However, there are two main limitations in the existing Back-of-Device (BoD) gesture recognition systems. They can only handle coarse-grained gesture recognition such as tap detection and cannot avoid the air-borne propagation suffering from the interference in the air. In this paper, we propose StruGesture, a fine-grained gesture recognition system using the back of mobile phones with ultrasonic signals. The key technique is to use the structure-borne sounds (i.e., sound propagation via structure of the device) to recognize sliding gestures on the back of mobile phones. StruGesture can fully extract the structure-borne component from the hybrid Channel Impulse Response (CIR) based on Peak Selection Algorithm. We develop a deep adversarial learning architecture to learn the gesture-specific representation for robust and effective recognition. Extensive experiments are designed to evaluate the robustness over nine deployment scenarios. The results show that StruGesture outperforms the competitive state-of-the-art classifiers by achieving an average recognition accuracy of 99.5% over 10 gestures.