Ultrasensitive optical modulation in hybrid metal-perovskite and metal-graphene metasurface THz devices.

Implementation of efficient terahertz (THz) wave control is essential for THz technology development for applications including sixth-generation communications and THz sensing. Therefore, realization of tunable THz devices with large-scale intensity modulation capabilities is highly desirable. By integrating perovskite and graphene with a metallic asymmetric metasurface, two ultrasensitive devices for dynamic THz wave manipulation through low-power optical excitation are demonstrated experimentally here. The perovskite-based hybrid metadevice offers ultrasensitive modulation with a maximum modulation depth for the transmission amplitude reaching 190.2% at the low optical pump power of 5.90 mW/cm2. Additionally, a maximum modulation depth of 227.11% is achieved in the graphene-based hybrid metadevice at a power density of 18.87 mW/cm2. This work paves the way toward design and development of ultrasensitive devices for optical modulation of THz waves.

A New Regularized Spatiotemporal Attention-Based LSTM with Application to Nitrogen Oxides Emission Prediction.

The data collected from complex process industries are usually time series with considerable nonlinearities and dynamics, as well as excessive redundancy. Moreover, there are temporal and spatial correlations between input variables and key performance variables. These characteristics bring great difficulties to data-driven modeling of the key performance variables. To overcome the problems, a new regularized spatiotemporal attention (STA)-based long short-term memory (LSTM) was developed. First, a standard LSTM network with an STA module was trained to capture the dynamic relationship between input and target variables. Second, the least absolute shrinkage and selection operator was introduced to optimize the STA module. Third, the hyperparameter representing the regularization strength of the algorithm was determined using a moving window cross-validation strategy. Finally, the proposed algorithm was compared to other state-of-the-art algorithms using artificial data, and then it was used to predict the nitrogen oxide emissions of a selective catalytic reduction denitration system. Simulation results showed that the proposed algorithm achieved more accurate predictions than the other algorithms. Furthermore, the statistics and analysis of the importance of the variables are consistent with known chemical-reaction mechanisms and observations of field experts. Thus, the proposed method can provide technical support for the predictive control and optimization of such systems.

Corrigendum: Improved real-time semantic segmentation network model for crop vision navigation line detection.

[This corrects the article DOI: 10.3389/fpls.2022.898131.].

Calibration of Ultrasonic Transducer Based on Ultrasonic Logging Instrument for Shaft Sinking.

High-precision logging equipment is critical for measuring the borehole diameter and drilling offset in coal mining and petroleum drilling. We propose a module composition and positioning principle for an ultrasonic transducer based on an ultrasonic logging instrument for shaft sinking by drilling (ULISSD) for calculating the reflection distance. The logging distance, which is the primary performance index of a logging system, is determined by using the self-reception sensitivity and error of the ultrasonic transducer in a downhole system. To measure the error between the piezoelectric element of the transducer and the rubber seal of the borehole logging system, we developed an ultrasonic-transducer error-calibration device and a calibration method for a central-air-return-shaft-drilling project. This calibration device can eliminate the inherent error of the transducer and calculate the rate of propagation with high accuracy. The measurement error is reduced by approximately 1.5 mm; thus, the ULISSD measurement accuracy can be effectively improved in central-air-return-shaft drilling.

Improved Real-Time Semantic Segmentation Network Model for Crop Vision Navigation Line Detection.

Field crops are generally planted in rows to improve planting efficiency and facilitate field management. Therefore, automatic detection of crop planting rows is of great significance for achieving autonomous navigation and precise spraying in intelligent agricultural machinery and is an important part of smart agricultural management. To study the visual navigation line extraction technology of unmanned aerial vehicles (UAVs) in farmland environments and realize real-time precise farmland UAV operations, we propose an improved ENet semantic segmentation network model to perform row segmentation of farmland images. Considering the lightweight and low complexity requirements of the network for crop row detection, the traditional network is compressed and replaced by convolution. Based on the residual network, we designed a network structure of the shunting process, in which low-dimensional boundary information in the feature extraction process is passed backward using the residual stream, allowing efficient extraction of low-dimensional information and significantly improving the accuracy of boundary locations and row-to-row segmentation of farmland crops. According to the characteristics of the segmented image, an improved random sampling consensus algorithm is proposed to extract the navigation line, define a new model-scoring index, find the best point set, and use the least-squares method to fit the navigation line. The experimental results showed that the proposed algorithm allows accurate and efficient extraction of farmland navigation lines, and it has the technical advantages of strong robustness and high applicability. The algorithm can provide technical support for the subsequent quasi-flight of agricultural UAVs in farmland operations.

Tunable bifunctional polarization-independent metamaterial device based on Dirac semimetal and vanadium dioxide.

In this study, a tunable bifunctional polarization-independent metamaterial device based on Dirac semimetal films (DSFs) and vanadium dioxide (VO2) is investigated. At the VO2 insulator state, a polarization-independent electromagnetically induced reflectance effect can be achieved via destructive interference between bright and dark modes. When VO2 transitions to a metallic state, the proposed device behaves as a dual-band polarization-independent absorber with 99.9% and 94.5% absorptance at 9.06 and 10.9 THz, respectively, and is insensitive over a wide range of incidence angles. In both cases, refractive index sensing is achieved, and the response can be dynamically tuned by changing the Fermi energy of the DSF.

Tunable bifunctional terahertz metamaterial device based on Dirac semimetals and vanadium dioxide.

A tunable bifunctional terahertz (THz) metamaterial device based on Dirac semimetal films (DSFs) and VO2 is presented. The insulator-to-metal phase transition of VO2 enables bifunctional asymmetric transmission and dual-directional absorption to be switched in the THz range. When VO2 serves as a dielectric, tunable broadband asymmetric transmission of linearly polarized THz waves can be achieved. When VO2 is in a metallic state, the proposed device acts as a tunable dual-directional absorber with perfect absorption in both illumination directions. In each case, the response can be tuned by varying the Fermi energy of the DSFs. This offers a new pathway for the development of tunable multifunctional THz metamaterial devices.

Tunable terahertz metamaterial absorber based on Dirac semimetal films.

In this paper, the tunable properties of metamaterial absorbers based on 3D Dirac semimetal films (DSFs) in the terahertz (THz) regime are discussed in theory. We consider the absorbers with square-shaped, circular-patch, and cross-shaped resonators. These resonances are theoretically polarization-insensitive at normal incidence because of their 90° rotational symmetry and can achieve perfect absorption in numerical simulation. We then introduce dual-band and broadband absorbers by combining two DSF-based square-shaped (or circular-patch) resonators into one unit cell with different sizes. Unlike with a conventional metal-based absorber, the absorption of a DSF-based absorber can be dynamically tuned by varying the Fermi energy instead of refabricating the structures. Moreover, the DSFs can be regarded as a "Salisbury screen" of an absorber to block the transmission at the THz frequencies, which can be more convenient than graphene in the application of a tunable absorber. Our designs have potential applications in various fields such as sensors, thermal detectors, and imagers.

A two-stage classification method for borehole-wall images with support vector machine.

Analyzing geological drilling hole images acquired by Axial View Panoramic Borehole Televiewer (APBT) is a key step to explore the geological structure in a geological exploration. Conventionally, the borehole images are examined by technicians, which is inefficient and subjective. In this paper, three dominant types of borehole-wall images on coal-rock mass structure, namely, border images, fracture images and intact rock mass images are mainly studied. The traditional image classification methods based on unified feature extraction algorithm and single classifier is not effect for the borehole images. Therefore, this paper proposes a novel two-stage classification approach to improve the classification performance of borehole images. In the first-stage classification, the border images are identified from three kinds of images based on texture features and gray-scale histograms features. For the remaining two types of images, in the second-stage classification, Gabor filter is first applied to segment the region of interest (ROI) (such as microfracture, absciss layer and horizontal cracks, etc.) and the central interference region. Then, using the same feature vector after eliminating the central interference region, fracture images are separated from intact rock mass images. We test our two-stage classification system with real borehole images. The results of experimental show that the two-stage classification method can effectively classify three major borehole-wall images with the correction rate of 95.55% in the first stage and 95% in the second stage.

Application of a Saddle-Type Eddy Current Sensor in Steel Ball Surface-Defect Inspection.

Steel ball surface-defect inspection was performed by using a new saddle-type eddy current sensor (SECS), which included a saddle coil and a signal conditioning circuit. The saddle coil was directly wound on the steel ball's outer bracket in a semi-circumferential direction. Driven by a friction wheel, the test steel ball rotated in a one-dimensional direction, such that the steel ball surface was fully scanned by the SECS. There were two purposes for using the SECS in the steel ball inspection system: one was to reduce the complexity of the unfolding wheel of the surface deployment mechanism, and the other was to reduce the difficulty of sensor processing and installation. Experiments were carried out on bearing steel balls in diameter of 8 mm with three types of representative and typical defects by using the SECS, and the results showed that the inspection system can detect surface defects as small as 0.05 mm in width and 0.1 mm in depth with high-repetition detection accuracy, and the detection efficiency of 5 pcs/s, which meet the requirement for inspecting ISO grade 10 bearing steel balls. The feasibility of detecting steel ball surface defects by SECS was verified.

A Steel Ball Surface Quality Inspection Method Based on a Circumferential Eddy Current Array Sensor.

To efficiently inspect surface defects on steel ball bearings, a new method based on a circumferential eddy current array (CECA) sensor was proposed here. The best probe configuration, in terms of the coil quality factor (Q-factor), magnetic field intensity, and induced eddy current density on the surface of a sample steel ball, was determined using 3-, 4-, 5-, and 6-coil probes, for analysis and comparison. The optimal lift-off from the measured steel ball, the number of probe coils, and the frequency of excitation current suitable for steel ball inspection were obtained. Using the resulting CECA sensor to inspect 46,126 steel balls showed a miss rate of ~0.02%. The sensor was inspected for surface defects as small as 0.05 mm in width and 0.1 mm in depth.

[The role of electronic techniques for advanced neuroelectrophysiology].

The rapid development in the fields of electroscience, computer science, and biomedical engineering are propelling the electrophysiologyical techniques. Recent technological advances have made it possible to simultaneously record the activity of large numbers of neurons in awake and behaving animals using implanted extracellular electrodes. Several laboratories use chronically implanted electrode arrays in freely moving animals because they allow stable recordings of discriminated single neurons and/or field potentials from up to hundreds of electrodes over long time periods. In this review, we focus on the new technologies for neuroelectrophysiology.

Features of central projection average difference function for skull recognition.

The recognition technology based on biological feature is a hot topic in recent years, and the recognition technology concerning the skull features was studied in this paper. The central projection transform about the two-dimension skull image was performed, the central projection function of the gray value was obtained, the average difference function of the central projection value and it's vectors were defined, and the research on the vector feature of the average difference function was carried out. This feature is robust in noisy backgrounds, less calculation consuming, and it can also be used to recognize other objects.

Segmenting and counting of wall-pasted cells based on gabor filter.

Correctly counting the live cells plays a great role in the ectogenetic anti-virus experiment. According to the irregular shape and arbitrary size of the wall pasted Hela cells overlapping each other, we propose a scheme to segment and count the cells using Gabor filter with different parameters and Morphological operation. Experiments reveal that filters with different parameters will lead to different results and a better segmentation will be achieved based on the characteristics of cells and optimal parameters. Large amount of experiment results show that this algorithm can successfully segment the cells and the accuracy arrives at 99.3%. This scheme based on image analysis and pattern recognition can overcome some disadvantages of traditional approaches, shortening anti-virus experimental period and reducing experimental cost.