Guilin University

Dual-band bandpass filters using novel stub-loaded resonators (SLRs) are presented in this letter. Characterized by both theoretical analysis and full-wave simulation, the proposed SLR is found to have the advantage that the even-mode resonant frequencies can be flexibly controlled whereas the odd-mode resonant frequencies are fixed. Based on the proposed SLR, a dual-band filter is implemented with three transmission zeros. To further improve the selectivity, a filter with four transmission zeros on either side of both passbands is designed by introducing spur-line. The measured results validate the proposed design.

Various MOFs with tailored nanoporosities have recently been developed as potential storage media for CO2 and H2. The composites based on Cu-BTC and graphene layers were prepared with different percentages of graphene oxide (GO). Through the characterization analyses and gas adsorption experiments, we found that the nanosized and well-dispersed Cu-BTC induced by the incorporation of GO greatly improved the carbon dioxide capture and hydrogen storage performance of the composites. The materials obtained exhibited about a 30% increase in CO2 and H2 storage capacity (from 6.39 mmol g−1 of Cu-BTC to 8.26 mmol g−1 of CG-9 at 273 K and 1 atm for CO2; from 2.81 wt% of Cu-BTC to 3.58 wt% of CG-9 at 77 K and 42 atm for H2). Finally, the CO2/CH4 and CO2/N2 selectivities were calculated according to single-component gas sorption experiment data.

We propose an active metasurface whose functionalities can be dynamically switched among linear-to-linear, linearto-elliptical, and linear-to-circular polarization conversions in a wideband. The active metasurface is composed of butterflyshaped unit cells embedded with voltage-controlled varactor diodes. By controlling the bias voltage of the varactor diodes, the electromagnetic responses of the proposed metasurface can be tailored, leading to reconfigurable polarization conversions. The simulation results reveal that with no bias voltage, the proposed metasurface is able to reflect linear-polarization waves to crosspolarization waves in the frequency range from 3.9 to 7.9 GHz, with a polarization conversion ratio of over 80%; however, at the bias voltage of -19 V, the metasurface is tuned to be a circular polarization converter in a wideband from 4.9 to 8.2 GHz. Moreover, two equivalent circuits along the x- and y-directions are developed to elucidate the tunable mechanism. The experimental results are in a good agreement with the simulation results obtained from commercial software and from the equivalent circuit model.

Intelligent fault diagnosis methods based on deep learning becomes a research hotspot in the fault diagnosis field. Automatically and accurately identifying the incipient micro-fault of rotating machinery, especially for fault orientations and severity degree, is still a major challenge in the field of intelligent fault diagnosis. The traditional fault diagnosis methods rely on the manual feature extraction of engineers with prior knowledge. To effectively identify an incipient fault in rotating machinery, this paper proposes a novel method, namely improved the convolutional neural network-support vector machine (CNN-SVM) method. This method improves the traditional convolutional neural network (CNN) model structure by introducing the global average pooling technology and SVM. Firstly, the temporal and spatial multichannel raw data from multiple sensors is directly input into the improved CNN-Softmax model for the training of the CNN model. Secondly, the improved CNN are used for extracting representative features from the raw fault data. Finally, the extracted sparse representative feature vectors are input into SVM for fault classification. The proposed method is applied to the diagnosis multichannel vibration signal monitoring data of a rolling bearing. The results confirm that the proposed method is more effective than other existing intelligence diagnosis methods including SVM, K-nearest neighbor, back-propagation neural network, deep BP neural network, and traditional CNN.

Metal–organic frameworks (MOFs), which are constructed from the assembly of organic ligands with metal ions or metal clusters, have high potential applications in the fields of gas storage, separations and catalysis. MOFs involving mesopores are considered to have specific performance in such fields. In this mini review, we are mainly focussing on the recent developments in mesoporous MOFs including the design strategies and their most important applications.

Chemical composition and film quality are two key figures of merit for large-area high-efficiency perovskite solar cells. To date, all studies on mixed perovskites have used solution-processing, which results in imperfect surface coverage and pin-holes generated during solvent evaporation, execrably influencing the stability and efficiency of perovskite solar cells. Herein, we report our development using a vacuum co-evaporation deposition method to fabricate pin-hole-free cesium (Cs)-substituted perovskite films with complete surface coverage. Apart from the simplified procedure, the present method also promises tunable band gap, reduced trap-state density and longer carrier lifetime, leading to solar cell efficiency as high as 20.13%, which is among the highest reported for planar perovskite solar cells. The splendid performance is attributed to superior merits of the Cs-substituted perovskite film including tunable band gap, reduced trap-state density and longer carrier lifetime. Moreover, the Cs-substituted perovskite device without encapsulation exhibits significantly higher stability in ambient air compared with the single-component counterpart. When the Cs-substituted perovskite solar cells are stored in dark for one year, the PCE remains at 19.25%, degrading only 4.37% of the initial efficiency. The excellent stability originates from reduced lattice constant and relaxed strain in perovskite lattice by incorporating Cs cations into the crystal lattice, as demonstrated by the positive peak shifts and reduced peak width in X-ray diffraction analysis.

In this paper, a three-node wireless powered communication system is studied, where a power receiver harvests energy from a wireless power transmitter via wireless power transfer in the downlink and then executes information transfer in the uplink. According to this simple harvest-then-transmit protocol, the power receiver cannot harvest energy and send information simultaneously. Thus, it is necessary to investigate the tradeoff between wireless power transfer and information transfer to obtain good performance. The goal of this paper is to achieve the maximum throughput by balancing the time duration between the wireless power transfer phase and the information transfer phase while satisfying the energy causality constraint, the time duration constraint, and the quality-of-service constraint (i.e., the symbol error rate is lower than a target value). By solving the optimization problem, an optimal time allocation can be obtained. Simulation results demonstrate the effectiveness of the solution.

A novel convolutional neural network namely the modified CNN-GAP model is proposed for fast fault diagnosis of the DC-DC inverter. This method improves the model structure of the traditional CNN by using a global average pooling layer to replace the fully connected layer of 2~3 layers. The improved CNN-GAP method mainly contains an input layer, a feature extraction layer, a global average pooling (GAP) layer, and a Softmax output layer. Firstly, the raw 1-D time-series data directly input into the input layer of the established CNN-GAP diagnosis model. The 2-D feature maps are reconstructed in the input layer. Secondly, the representative features are automatically extracted from the 2-D feature maps by using multiple convolutional layers and pooling layers. Thirdly, the dimension transformation and size compression of the output image of the feature extraction layer is completed by the GAP layer. Finally, the fault diagnosis result of the DC-DC inverter is automatically output in the Softmax output layer. The proposed method is used for diagnosing the open-circuit fault of the IGBT in the isolated DC-DC inverter. The proposed method is more accurate and effective than other mainstream intelligent diagnosis methods including the SVM, KNN, DNN, and traditional CNN. The experiment results show that the diagnostic accuracy is up to 99.95%, and the testing time can reduce by more than 15%. The improved CNN-GAP method could greatly reduce the model parameter quantity of the traditional CNN more than 80%, which is more suitable for rapid fault diagnosis in electronic devices.

Scheduling is a very important part of the cloud computing system. This paper introduces an optimized algorithm for task scheduling based on genetic simulated annealing algorithm in cloud computing and its implementation. Algorithm considers the QOS requirements of different type tasks, the QOS parameters are dealt with dimensionless. The algorithm efficiently completes tasks scheduling in the cloud computing environment computing.

) were ultimately obtained through random forest feature selection. Cox regression analysis confirmed the 6-gene signature is an independent prognostic factor in HNSCC patients. This signature effectively stratified samples in the training, test, and external verification sets (P < 0.01). The 5-year survival AUC in the training and verification sets was greater than 0.74. Thus, we have constructed a 6-gene signature as a new prognostic marker for predicting survival of HNSCC patients.

In this paper, we present interference models for cognitive radio (CR) networks employing various interference management mechanisms including power control, contention control or hybrid power/contention control schemes. For the first case, a power control scheme is proposed to govern the transmission power of a CR node. For the second one, a contention control scheme at the media access control (MAC) layer, based on carrier sense multiple access with collision avoidance (CSMA/CA), is proposed to coordinate the operation of CR nodes with transmission requests. The probability density functions (PDFs) of the interference received at a primary receiver from a CR network are first derived numerically for these two cases. For the hybrid case, where power and contention controls are jointly adopted by a CR node to govern its transmission, the interference is analyzed and compared with that of the first two schemes by simulations. Then, the interference PDFs under the first two control schemes are fitted by log-normal PDFs to reduce computation complexity. Moreover, the effect of a hidden primary receiver on the interference experienced at the receiver is investigated. It is demonstrated that both power and contention controls are effective approaches to alleviate the interference caused by CR networks. Some in-depth analysis of the impact of key parameters on the interference of CR networks is given as well.

Although many efforts have been made on the fusion of Light Detection and Ranging (LiDAR) and aerial imagery for the extraction of houses, little research on taking advantage of a building's geometric features, properties, and structures for assisting the further fusion of the two types of data has been made. For this reason, this paper develops a seamless fusion between LiDAR and aerial imagery on the basis of aspect graphs, which utilize the features of houses, such as geometry, structures, and shapes. First, 3-D primitives, standing for houses, are chosen, and their projections are represented by the aspects. A hierarchical aspect graph is then constructed using aerial image processing in combination with the results of LiDAR data processing. In the aspect graph, the note represents the face aspect and the arc is described by attributes obtained by the formulated coding regulations, and the coregistration between the aspect and LiDAR data is implemented. As a consequence, the aspects and/or the aspect graph are interpreted for the extraction of houses, and then the houses are fitted using a planar equation for creating a digital building model (DBM). The experimental field, which is located in Wytheville, VA, is used to evaluate the proposed method. The experimental results demonstrated that the proposed method is capable of effectively extracting houses at a successful rate of 93%, as compared with another method, which is 82% effective when LiDAR spacing is approximately 7.3 by 7.3 ft <sup xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">2</sup> . The accuracy of 3-D DBM is higher than the method using only single LiDAR data.

This paper proposes a novel intrusion detection approach by applying ant colony optimization for feature selection and SVM for detection. The intrusion features are represented as graph-ere nodes, with the edges between them denoting the adding of the next feature. Ants traverse through the graph to add nodes until the stopping criterion is satisfied. The fisher discrimination rate is adopted as the heuristic information for ants' traversal. In order to avoid training of a large number of SVM classifier, the least square based SVM estimation is adopted. Initially, the SVM is trained based on grid search method to obtain discrimination function using the training data based on all features available. Then the feature subset produced during the ACO search process is evaluated based on their abilities to reconstruct the reference discriminative function using linear least square estimation. Finally SVM is retrained using the train data based on the obtained optimal feature subset to obtain intrusion detection model. The MIT's KDD Cup 99 dataset is used to evaluate our present method, the results clearly demonstrate that the method can be an effective way for intrusion feature selection and detection.

The thesis made a brief summary of the development process of the user interface. through the some examples,describe the disappearance of sense of entities and the arrival of natural user interface, and focuses on the seven typical characteristics of the natural interface.

A robust and efficient vehicle detection method from high resolution aerial image is still challenging. In this paper, a novel and robust method for automatic vehicle detection using aerial images over highway was presented. In the method, a GIS road vector map was used to constrain the vehicle detection system to the highway networks. After the morphological structure element was identified, we utilized the grayscale opening transformation and grayscale top-hat transformation to identify hypothesis vehicles in the light or white background, and used the grayscale closing transformation and grayscale bot-hat transformation to identify the hypothesis vehicles in the black or dark background. Then, targets with large size or covering a large area were sieved from the hypothesis vehicles using an area threshold that is much larger than a typical vehicle. Targets, whose width is narrower than the diameter of structure element utilized in the grayscale morphological transformation, were smoothed out from the hypothesis vehicles using binary morphological opening transformation. Finally, the hypothesis vehicles detected in both cases were overlaid. It should be noted that in the detection system, a vehicle could be detected twice by the two approaches. The two identical hypothesis vehicles should be amalgamated into a single one for accuracy assessment subsequently. We tested our system on seventeen highway scenes of aerial images with a spatial resolution of 0.15 × 0.15 m. The experimental results showed that the correctness, completeness, and quality rates of the proposed vehicle detection method were about 98%, 93%, and 92%, respectively. Thus, our proposed approach is robust and efficient to detect vehicles of highway using high resolution aerial images.

In this article, a modified convolutional neural network (CNN) algorithm, namely 1D-GAPCNN-SVM, is proposed to address the early anomaly diagnosis problem. Considering the fact that traditional 2D-CNN based approaches contain too many model parameters and are not suitable for fast diagnosis applications using multisensor 1-D time-series measurements, 1D-CNN is introduced to deal with this problem. To reduce the number of parameters, a 1-D global average pooling layer is designed to substitute the fully connected layer with two or three layers. In order to further improve the diagnosis accuracy, a nonlinear multiclass support vector machine (SVM) is adopted to replace the traditional Softmax classifier as the final discriminator. Raw multisensor 1-D time-series data are directly fed into the diagnosis model, then the diagnosis result can be automatically generated. Two experiments, which are rolling bearing and GPS anomaly detection, have been conducted to demonstrate the effectiveness and the superior performance of the proposed method compared to the conventional SVM, K-nearest neighbor, deep neural network (DNN), and traditional 2D-CNN.

In this paper, a cavity-backed folded triangular bowtie antenna (FTBA) is proposed and investigated. Comparisons show that it has much smaller fluctuation of input impedance and much larger bandwidth (BW) for stable radiation patterns than cavity-backed ordinary TBA. The proposed antenna is fed by a conventional balun composed of a transition from a microstrip line to a parallel stripline. It achieves an impedance BW of 92.2% for |S <sub xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">11</sub> | les -10 dB, stable gain of around 9.5 dBi and unidirectional radiation patterns over the whole operating band. A close examination of the antenna aperture efficiency and the electric field distribution in the aperture provides an explanation for the stable gain across such large frequency band. Comparisons are made against several conventional antennas, including short backfire and microstrip patch antennas, to illustrate the salient features of the proposed antenna. A design guideline is also given for practical applications.

This paper concerns the uplink of multicell multiuser multiple-input multiple-output (MIMO) systems. To tackle the effect of pilot contamination that is generally viewed as a bottleneck in previous work, this paper presents a superimposed pilot (SP)-aided uplink channel estimation scheme and mathematically characterizes the impact that an SP has on the performance of such a very large MIMO system. It is shown that there are two types of interference components that do not vanish, even when the number of antennas M grows to infinity. The first type, which is referred to as cross-contamination, is due to the correlation between the SP and data among different cells. The second type, which is referred to as self-contamination, is due to the dependence between channel estimation and estimation error. Cross-contamination is, in principle, similar to pilot contamination in a conventional pilot-based multicell MIMO system, whereas self-contamination is unique for the SP-aided scheme. Both theoretical and simulation results demonstrate that the SP-aided scheme can effectively reduce the estimation contamination by increasing the data frame size and, in turn, achieve a significant improvement over the spectral efficiency, in comparison with conventional pilot-based methods.

The exponential growth of the blockchain size has become a major contributing factor that hinders the decentralisation of blockchain and its potential implementations in data-heavy applications. In this paper, we propose segment blockchain, an approach that segmentises blockchain and enables nodes to only store a copy of one blockchain segment. We use PoW as a membership threshold to limit the number of nodes taken by an Adversary-the Adversary can only gain at most n/2 of nodes in a network of n nodes when it has 50% of the calculation power in the system (the Nakamoto blockchain security threshold). A segment blockchain system fails when an Adversary stores all copies of a segment, because the Adversary can then leave the system, causing a permanent loss of the segment. We theoretically prove that segment blockchain can sustain a (AD/n) <sup xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">m</sup> failure probability when the Adversary has no more than AD number of nodes and every segment is stored by m number of nodes. The storage requirement is mostly shrunken compared to the traditional design and therefore making the blockchain more suitable for data-heavy applications.

In this paper, we first introduce the model of single-driving double-response system (SDDRS), which consists of a driving system (subsystem) and two response systems (subsystems). By applying the theory of Lyapunov stability, we study the projective synchronization of SDDRS between the driving and response systems. The sufficient conditions for achieving projective synchronization are obtained when the driving system has either a globally stable equilibrium point or a chaotic attractor. Furthermore, we use the SDDRS for cryptography in secure communication and present a novel scheme for encryption and decryption based on its projection synchronization. The results of numerical simulations verify the efficiency of the presented control schemes and the excellence of cryptography.