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Aiming to the limitations of traditional reconfigurable antennas, this paper proposes a novel reconfigurable antenna that can achieve four polarizations and continuously-tunable frequency. The proposed antenna is based on liquid metal, i.e., eutectic gallium and indium (EGaIn). The pattern of the slots can be controlled by changing the amount of the liquid metal inserted in the 3-D printed channels. The antenna can work under four polarization modes: left-hand circular polarization (LHCP), right-hand circular polarization (RHCP), and two orthogonal linear polarization (LP) modes. Under the LP modes, the working frequency of the antenna can be tuned continuously from 1.82 GHz to 2.0 GHz. The measured results agree with the simulated ones, which validate the performance of proposed antenna.

Establishing accurate wireless propagation models is essential for high-quality communications. Aiming at the low accuracy and complexity of the traditional wireless propagation model, a novel accurate wireless propagation model is proposed based on the bi-directional long short-term memory (Bi-LSTM) algorithm of machine learning. The model uses machine learning technology driven by big data and can achieve high real-time performance with low complexity. Also, it can accurately predict the wireless signal coverage intensity in a new environment. To allow the model to accommodate the actual environment of target areas, the propagation model can be dynamically corrected by deep learning and training. The Bi-LSTM is used to describe the relationship between features themselves and the relationship between features and target values of reference signal receiving power (RSRP). The Bi-LSTM is also used to represent the relationship through a full-connection layer to obtain the results so that sufficient parameter space can be provided for the model. The propagation model parameters are searched and fitted through a full-connection optimization. After training and tuning, the model’s predicted value of poor coverage recognition rate (PCRR) can reach 0.2371, while the predicted value of root mean squared error (RMSE) can be 10.4855, which demonstrates the better accuracy of the proposed model.

As big data mining technology penetrates into various fields, cross-domain topics driven by data predictive analysis have become important entry points for solving traditional problems. Due to the complex changes of the pressure sensor and the interaction of different grouped trains during the train braking process, the mechanism modeling is difficult, the data is highly temporalized, and the data distribution is not stable. Facing the development trend of long-grouped-heavy-duty train captains, if the braking analysis of the train by temporal data mining of small groups can be used for predictive analysis, it will make innovative progress in the entire train braking field. This paper focuses on combining latest technology such as machine learning, transfer learning and lifelong learning to construct the first predictive analysis research framework in the field of train braking systems. Based on the principle of train braking process and temporal data collected from intelligent experiment platform, a baseline has firstly been built to solve fixed-grouped and multi-grouped temporal prediction problems. Then a predictive algorithm for model verification and update for lifelong learning is established to automatically update model parameters over time. Finally, relying on the parameter transfer in transfer learning, a multi-grouped temporal data prediction analysis is performed. Through comparing the training results of the “pre-trained” model on the general domain, the “tuned” model on both general domain and the target domain, and the “target only” model on the target domain separately, multi-domain tuning results show their applicable scope and transfer conditions. In summary, this work can contribute to intelligently upgrading the semi-physical intelligent test platform for long-grouped-heavy-duty trains.

Sensors are key components in structural health monitoring. Although significant improvements have been made on monitoring technologies by using many advanced smart sensors, the need of cables and power supplies still causes much inconvenience in the practical implementation of these technologies. In this paper, we propose a novel rectangular patch antenna to monitor the structural health in a wireless and passive manner so that the drawback of traditional monitoring methods can be fully overcome. The antenna is used as a sensor and its resonant frequency will change linearly in terms of its size changes in both horizontal and vertical directions. The antenna will transmit an electromagnetic wave with the resonant frequency information to a reader by a long distance and activate the chip in the reader. According to the received information about the resonant frequency, the change of the targeted structure's strain can be measured in real time and its health state can then be evaluated. Simulations and experiments show that the designed antenna is an excellent strain sensor and can be applied to the health detection for many types of structure.

In this response letter, we focus on addressing Yu Hang Yang’s comments so that the correctness and validity of the proposed auxiliary-differential-equation-based (ADE) Crank-Nicolson finite-difference time-domain method with direct-splitting technique (CNDS-FDTD) are demonstrated. Moreover, related applications in modeling 2D material nanostructure problems are shown.

This is a reply to the Comments proposed by Mahmoud Wagih on our published paper entitled “A Passive and Wireless Sensor Based on RFID Antenna for Detecting Mechanical Deformation”. His concerns about Fig. 6 in the paper are clarified and his good suggestion is appreciated.

Partially defective fingerprint image (PDFI) with poor performance poses challenges to the automated fingerprint identification system (AFIS). To improve the quality and the performance rate of PDFI, it is essential to use accurate segmentation. Currently, most fingerprint image segmentations use methods with ridge orientation, ridge frequency, coherence, variance, local gradient, etc. This paper proposes a method of XFinger-Net for segmenting PDFIs. Based on U-Net, XFinger-Net inherits its characteristics. The attention gate with fewer parameters is used to replace the cascaded network, which can suppress uncorrelated regions of PDFIs. Moreover, the XFinger-Net implements a pixel-level segmentation and takes non-blocking fingerprint images as an input to preserve the global characteristics of PDFIs. The XFinger-Net can achieve a very good segmentation effect as demonstrated in the self-made fingerprint segmentation test.