A Ka-band one-dimensional beam scanning leaky-wave antenna based on liquid crystal.

Fixed frequency beam-scanning leaky-wave antennas have been a focus of attention for many scholars in recent years, and numerous related results have been obtained. However, these antennas suffer from several issues such as small beam-scanning range, low gain, and unsatisfactory impedance matching. To address these problems, this paper proposes a microstrip line (ML) antenna unit based on liquid crystal (LC) materials etched Complementary Split Ring Resonator (CSRR). In a first-of-its-kind approach, the substrate integrated waveguide (SIW) structure and the ML transmission structure are combined to present the SIW-ML transmission structure. The antenna operates in the Ka-band with excellent resonance characteristics at 34.7 GHz, and the S11 parameters are below - 13 dB in the frequency range of 30-40 GHz, indicating outstanding impedance matching. By arranging 56 antenna units, a periodic leaky-wave antenna is created, enabling fixed-frequency beam-scanning at 34.7 GHz. Experimental results show that the antenna can achieve scanning of angles between - 53° and + 60° with a gain of up to 12.63 dB. Once single-beam scanning is achieved, a method combining LC and discrete amplitude weighting technique, as well as multi-beam theory, is proposed for multi-beam study. Experimental results reveal that the designed 56-unit beam-scanning antenna can effectively realize beam scanning in two directions.

A novel unsupervised dynamic feature domain adaptation strategy for cross-individual myoelectric gesture recognition.

Objective.Surface electromyography pattern recognition (sEMG-PR) is considered as a promising control method for human-machine interaction systems. However, the performance of a trained classifier would greatly degrade for novel users since sEMG signals are user-dependent and largely affected by a number of individual factors such as the quantity of subcutaneous fat and the skin impedance.Approach.To solve this issue, we proposed a novel unsupervised cross-individual motion recognition method that aligned sEMG features from different individuals by self-adaptive dimensional dynamic distribution adaptation (SD-DDA) in this study. In the method, both the distances of marginal and conditional distributions between source and target features were minimized through automatically selecting the optimal feature domain dimension by using a small amount of unlabeled target data.Main results.The effectiveness of the proposed method was tested on four different feature sets, and results showed that the average classification accuracy was improved by above 10% on our collected dataset with the best accuracy reached 90.4%. Compared to six kinds of classic transfer learning methods, the proposed method showed an outstanding performance with improvements of 3.2%-13.8%. Additionally, the proposed method achieved an approximate 9% improvement on a publicly available dataset.Significance.These results suggested that the proposed SD-DDA method is feasible for cross-individual motion intention recognition, which would provide help for the application of sEMG-PR based system.

Fabrication of the Fe-Doped Corona Schiff Base for Enhanced Microwave Absorption Performance.

A corolla-shaped Schiff base polymer was synthesized from terephthalaldehyde (TPAD), glutaraldehyde (GA), and p-phenylenediamine (PPD) by block copolymerization, and Schiff base iron complexes were formed by doping with FeCl3. The microscopic morphology, crystal structure, and elemental valence state were characterized by field emission scanning electron microscopy (FESEM), Fourier transform infrared spectroscopy (FTIR), X-ray diffraction (XRD), and X-ray photoelectron spectroscopy (XPS). Comparing the change of conductivity before and after Fe3+ doping, it was found that the conductivity did not break away from the category of insulator, and the doped sample is a paramagnetic material. Morphological changes were observed by adjusting the ratio of GA to TPAD, and it was found that the corolla-like structure was most complete when the ratio of GA to TPAD was 2:1, and its Schiff base iron complex absorbed waves better. At a thickness of 3 mm, the absorption effect can reach below -10 dB at 12.44-15.16 GHz, and the maximum absorption value is -45.07 dB at a thickness of 3.8 mm; it is an organic absorbing agent with excellent impedance matching and absorbing properties.

Utilization of Phytic Acid as a Selective Depressant for Quartz Activated by Zinc Ions in Smithsonite Flotation.

It is difficult to separate smithsonite from quartz with metal ion activation through flotation using sodium oleate (NaOL) as the collector. The inevitable Zn2+ in the flotation process of zinc oxide ore makes the separation of smithsonite and quartz more difficult. Thus, this study investigated the use of phytic acid (PA) as a flotation depressant to separate smithsonite from Zn2+-activated quartz while utilizing sodium oleate as the collector. Microflotation tests indicated that phytic acid could selectively inhibit the flotation of Zn2+-activated quartz without affecting the flotation of smithsonite. The measured zeta potentials revealed that the existence of phytic acid hindered sodium oleate adsorption to the surface of Zn2+-activated quartz but had little influence on the adsorption of smithsonite. Zn2+ dissolution tests and scanning electron microscopy coupled with energy-dispersive X-ray spectroscopy analysis indicated that the phytic acid could dissolve the Zn2+ from the minerals' surfaces into the solution. In conjunction with X-ray photoelectron spectroscopy results, the analysis indicated that phytic acid could adsorb onto the Zn2+-activated quartz surface and eliminate active sites for sodium oleate adsorption by dissolving the active Zn2+ from the quartz surface into the solution.

Multi-domain-fusion deep learning for automatic modulation recognition in spatial cognitive radio.

Automatic modulation recognition (AMR) is a critical technology in spatial cognitive radio (SCR), and building high-performance AMR model can achieve high classification accuracy of signals. AMR is a classification problem essentially, and deep learning has achieved excellent performance in various classification tasks. In recent years, joint recognition of multiple networks has become increasingly popular. In complex wireless environments, there are multiple signal types and diversity of characteristics between different signals. Also, the existence of multiple interference in wireless environment makes the signal characteristics more complex. It is difficult for a single network to accurately extract the unique features of all signals and achieve accurate classification. So, this article proposes a time-frequency domain joint recognition model that combines two deep learning networks (DLNs), to achieve higher accuracy AMR. A DLN named MCLDNN (multi-channel convolutional long short-term deep neural network) is trained on samples composed of in-phase and quadrature component (IQ) signals, to distinguish modulation modes that are relatively easy to identify. This paper proposes a BiGRU3 (three-layer bidirectional gated recurrent unit) network based on FFT as the second DLN. For signals with significant similarity in the time domain and significant differences in the frequency domain that are difficult to distinguish by the former DLN, such as AM-DSB and WBFM, FFT (Fast Fourier Transform) is used to obtain frequency domain amplitude and phase (FDAP) information. Experiments have shown that the BiGUR3 network has superior extraction performance for amplitude spectrum and phase spectrum features. Experiments are conducted on two publicly available datasets, the RML2016.10a and RML2016.10b, and the results show that the overall recognition accuracy of the proposed joint model reaches 94.94% and 96.69%, respectively. Compared to a single network, the recognition accuracy is significantly improved. At the same time, the recognition accuracy of AM-DSB and WBFM signals has been improved by 17% and 18.2%, respectively.

A Method of Noise Reduction for Radio Communication Signal Based on RaGAN.

Radio signals are polluted by noise in the process of channel transmission, which will lead to signal distortion. Noise reduction of radio signals is an effective means to eliminate the impact of noise. Using deep learning (DL) to denoise signals can reduce the dependence on artificial domain knowledge, while traditional signal-processing-based denoising methods often require knowledge of the artificial domain. Aiming at the problem of noise reduction of radio communication signals, a radio communication signal denoising method based on the relativistic average generative adversarial networks (RaGAN) is proposed in this paper. This method combines the bidirectional long short-term memory (Bi-LSTM) model, which is good at processing time-series data with RaGAN, and uses the weighted loss function to construct a noise reduction model suitable for radio communication signals, which realizes the end-to-end denoising of radio signals. The experimental results show that, compared with the existing methods, the proposed algorithm has significantly improved the noise reduction effect. In the case of a low signal-to-noise ratio (SNR), the signal modulation recognition accuracy is improved by about 10% after noise reduction.

A Joint Automatic Modulation Classification Scheme in Spatial Cognitive Communication.

Automatic modulation discrimination (AMC) is one of the critical technologies in spatial cognitive communication systems. Building a high-performance AMC model in intelligent receivers can help to realize adaptive signal synchronization and demodulation. However, tackling the intra-class diversity problem is challenging to AMC based on deep learning (DL), as 16QAM and 64QAM are not easily distinguished by DL networks. In order to overcome the problem, this paper proposes a joint AMC model that combines DL and expert features. In this model, the former builds a neural network that can extract the time series and phase features of in-phase and quadrature component (IQ) samples, which improves the feature extraction capability of the network in similar models; the latter achieves accurate classification of QAM signals by constructing effective feature parameters. Experimental results demonstrate that our proposed joint AMC model performs better than the benchmark networks. The classification accuracy is increased by 11.5% at a 10 dB signal-to-noise ratio (SNR). At the same time, it also improves the discrimination of QAM signals.

ResNet-AE for Radar Signal Anomaly Detection.

Radar signal anomaly detection is an effective method to detect potential threat targets. Given the low Accuracy of the traditional AE model and the complex network of GAN, an anomaly detection method based on ResNet-AE is proposed. In this method, CNN is used to extract features and learn the potential distribution law of data. LSTM is used to discover the time dependence of data. ResNet is used to alleviate the problem of gradient loss and improve the efficiency of the deep network. Firstly, the signal subsequence is extracted according to the pulse's rising edge and falling edge. Then, the normal radar signal data are used for model training, and the mean square error distance is used to calculate the error between the reconstructed data and the original data. Finally, the adaptive threshold is used to determine the anomaly. Experimental results show that the recognition Accuracy of this method can reach more than 85%. Compared with AE, CNN-AE, LSTM-AE, LSTM-GAN, LSTM-based VAE-GAN, and other models, Accuracy is increased by more than 4%, and it is improved in Precision, Recall, F1-score, and AUC. Moreover, the model has a simple structure, strong stability, and certain universality. It has good performance under different SNRs.

Novel N,Cl-doped deep eutectic solvents-based carbon dots as a selective fluorescent probe for determination of morphine in food.

In the present study, new N,Cl co-doped carbon dots (N,Cl-CDs) based on deep eutectic solvent (DES) were fabricated by a facile hydrothermal process. This fluorescent probe exhibited a good quantum yield of 14% and was applied for the sensitive and selective quantification of morphine in foods. In addition, the influence of solution pH, interaction time, system temperature, interfering substances and analogues on the determination was also investigated. Under the optimized conditions, the luminescence intensity of carbon dots increased linearly with the addition of morphine in the concentration range of (0.15-280.25) µg mL-1 (R 2 > 0.9969) and the limit of detection (LOD) of 46.5 ng mL-1. Based on these results, it is suggested that N,Cl-CDs is a promising fluorescent probe for sensitive and selective quantification of morphine in foods.

[Influence of post-core on the strength of endodontically treated and crowned teeth].

OBJECTIVE: To investigate the influence of post-core structure on the strength of endodontically treated and crowned teeth with or without a 2.0 mm dentine ferrule. METHODS: A total of 60 recently extracted human maxillary central incisors were endodontically treated and randomly divided into five groups of 12. They were given following treatments: Group A, endodontically treated; Group B, endodontically treated and crowned (PFM); Group C, cast metal post-core with 2.0 mm dentine ferrule and crowned (PFM); Group D, cast metal post-core with no dentine ferrule and crowned (PFM); Group E, prefabricated post and composite core with 2.0 mm dentine ferrule and crowned (PFM). All specimens were stored at 100% humidity at room temperature for 30 days before testing. Each specimen was in a special jig on the MTS 810 universal material testing machine and subjected to a load at a 135-degree angle to the long axis until failure, with crosshead speed of 0.02 cm/minute. Analysis of variance followed by the Newman-Keuls pairwise multiple comparison tests was used to compare the results. RESULTS: There was a statistically significant difference between different restorative methods. The cast metal post-core with 2.0 mm dentine ferrule and crowned teeth had the highest fracture strength (1793.59 +/- 387.93N), followed by endodontically treated intact teeth (1466.68 +/- 240.11N). No significant difference in the fracture strength was found among the other three groups (958.49 +/- 286.02N; 992.98 +/- 291.00N; 994.94 +/- 285.04 N). There was a statistically significant difference in the fracture resistance between crowned teeth with and without 2.0 mm dentine ferrule (P < 0.01). CONCLUSIONS: Not all post-core structure could improve the strength of endodontically treated teeth. The dentine ferrule can effectively improve the fracture resistance of endodontically treated and crowned teeth.