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In this communication, a pattern-reconfigurable filtering patch antenna is presented which consists of a driven patch, a stacked patch, an embedded filtering feeding line, and two vertical switchable elements with gaps from the suspended patches. Compared with the conventional stacked patch antenna, the proposed antenna does not increase the volume. A novel filtering feeding network is designed which consists of a hairpin-shaped resonator and a half-wavelength coupled microstrip line. It is used to generate the radiation nulls near the lower and higher passband edges. A U-shaped slot is etched on the driven patch to generate another radiation null. Three radiation nulls are excited to realize filtering performance. Meanwhile, the radiation beam of the proposed antenna can be reconfigured by switching the function of switchable elements between a director and a reflector using p-i-n diodes. Good radiation pattern reconfigurability, filtering, and wideband performance can be achieved simultaneously. The simulated and measured results show that the radiation beam can be switched to the directions of −33°, 33°, and 0°. The proposed antenna can maintain a wide operating bandwidth of 2.22–2.84 GHz (25%), out-of-band suppression of over 14 dB, and flat in-band gain of about 8 dBi in different states. [ABSTRACT FROM AUTHOR]

MEMS safety and arming device (SAD) with merits of small size and easy integration is one of the most promising candidates for the fuze to realize miniaturization, integration and intelligence. In this paper, a novel inertia-driven MEMS SAD with three independent safety features is proposed. The SAD placed perpendicular to the projectile axis is beneficial to reduce the size and improve the detonation energy transfer efficiency. The dynamic finite element simulations are carried out to study the mechanical responses of the device under dual-environment inertial loads. The response behaviors of the setback and centrifugal safety features and interrupter under different loads are elucidated and discussed. A series of experiments are performed to verify the simulation analysis results and investigate the safety and arming performances. The results indicate that the proposed MEMS SAD with small size and compact structure has successfully achieved safety and arming performances. The experimental results are in good agreement with the simulated analysis results. [ABSTRACT FROM AUTHOR]

This article presents a shared-aperture dual-band dual-polarized antenna array for fifth-generation (5G) mobile communication. Within one radiating aperture, four higher band (HB) patch antenna elements are placed over the lower band (LB) full-wavelength slot antenna. The proposed array is miniaturized by bending the ground plane of the slot antenna. Suppression of cross-band mutual coupling and scattering is achieved by using the filtering performance of the slot antenna and a dual-function metasurface. The filtering performance of the slot antenna is realized using coupled microstrip lines inside the radiating slot and open-ended stubs on the feeding lines. The dual-function metasurface operates as an artificial magnetic conductor reflector in the LB and bandpass frequency-selective surface in the HB. By placing the dual-function metasurface behind the slot antenna, the radiation of the slot antenna is enhanced in the LB but not HB, and the negative effect of its HB scattering on the HB antennas is reduced. The working mechanism is explained in detail. The measured and simulated results demonstrate that the proposed array has stable radiation patterns in the bands of 0.69–0.96 and 3.4–3.7 GHz with a high cross-band isolation of over 32 dB and is more compact than the previous works. [ABSTRACT FROM AUTHOR]

Semisupervised deep learning methods (DLMs) can mitigate the dependence on large amounts of labeled samples using a small number of labeled samples. However, for semisupervised deep feature learning (SDFL), the quality of extracted features cannot be well ensured without a certain amount of labeled samples. To address this issue, we develop the SDFL method with feature consistency (SDFL-FC) for the hyperspectral image (HSI) classification. The SDFL-FC first adopts the convolutional neural network (CNN) to extract spectral–spatial features of HSI and then uses the fully connected layers (FCLs) to model the feature consistency. Moreover, two constraints that enforce both the feature consistency of single pixel (FCS) and feature consistency of group pixels (FCG) are introduced to obtain the representative and discriminative features. The FCS is achieved by the generative adversarial network (GAN) regularization, which can reconstruct the original data from extracted features. The FCG is based on the assumption that the features of group pixels should have similar characteristics within a superpixel, which is embedded in each FCL. The final FCL outputs the class labels, and the cross-entropy (CE) loss is calculated with the labeled samples, while the two losses of FCS and FCG are calculated with all the training samples (both labeled and unlabeled). SDFL-FC integrates the FCS, FCG, and CE loss into a unified objective function and uses a customized iterative optimization algorithm to optimize it. Experiments demonstrate that the SDFL-FC can outperform the related state-of-the-art HSI classification methods. [ABSTRACT FROM AUTHOR]

Dynamic performance of microelectromechanical system (MEMS) electrothermal actuators is directly determined by thermal principle and thermal characteristics at microscale differed from macroscale. To study and deeply understand the heat transfer mechanism and dynamic behavior of the MEMS electrothermal actuators, dynamic thermal measurements of a U-shaped thermal actuator and its array are implemented in air by using an infrared thermal microscope system with high spatial resolution. Both the steady-state, transient and frequency response thermal measurements are thoroughly carried out for the actuators. The temperature distribution, heating and cooling performances, and frequency response at different waveform voltages are analyzed and discussed. Furthermore, the electro-thermo-structural coupling model is established and the simulated results are compared with the measured results including steady-state, transient and frequency response thermal performances. By qualitative and quantitative comparisons, the simulated results are in fairly good agreement with the measured results. [ABSTRACT FROM AUTHOR]

To cope with the abuse of steganography using H.264 videos, i.e., the dominant video format, as the carrier, this paper presents a steganalytic method which works well even in the scenario where both the training data and the prior knowledge of the test data are limited. As a key feature of H.264, intra prediction is incorporated to remove redundancies within one single frame by predicting the current block using previously coded blocks. Unlike in JPEG domain, the quantized discrete cosine transform (QDCT) coefficients in H.264 videos come from the prediction error (residual) blocks (PEBs) instead of the original pixel block, hence we suggest shifting the focal point from the spatial domain to the prediction error domain, i.e., the PEB domain. According to the traits of video coding, 3 types of subtractive PEB (SPEB) are defined to capture the inconsistency between correlated PEBs, and the differences between correlated SPEBs are modeled by first-order Markov chain. Then the so-called SUPERB (SUbtractive Prediction ERror Block) features are engineered by subsets of sample transition probability matrices for a steganalyzer. What’s more, the features derived from IPM (Intra Prediction Mode) transition probabilities are also merged into SUPERB to improve detection ability. Extensive experiments are carried out from different aspects. Performance results demonstrate the effectiveness of SUPERB, particularly its essence of general applicability when the training and test data are of quite different attributes, which is more favorable for real-world applications. [ABSTRACT FROM AUTHOR]

This paper proposes a novel coding strategy to achieve distortion minimization for H.264 steganography with quantized discrete cosine transform (QDCT) coefficients. Currently, with the help of syndrome-trellis codes (STCs), state-of-the-art image steganography embeds messages while minimizing a heuristically defined distortion function. However, this concept cannot be directly ported to steganography using compressed video as the cover media. According to the intra prediction principle, an H.264 QDCT coefficient block is predicted and coded based on previously encoded blocks, so even a slight embedding change will set off a chain reaction in the remaining cover blocks. Considering the cover block dependency, we make necessary changes to the standard trellis coding structure so as to be applicable for the joint compression embedding scenario. During the coding/embedding procedure, we maintain multiple contexts corresponding to possible optimal routes, and retrace each route periodically to determine how each cover block should be modified. After each modification, the remaining cover blocks, as well as their embedding costs, are re-evaluated, and each context is updated to reflect the embedding effect. In this way, the global optimality can be approached progressively in a block-by-block manner, so our proposed method is named progressive trellis coding (PTC). Extensive experiments have been conducted, and corresponding results show that the adoption of PTC brings about a significant gain in embedding performance. [ABSTRACT FROM AUTHOR]

This letter reports an inertial microswitch with an integrated filtering mechanism to realize acceleration band-pass characteristics. The switch is composed of a normal acceleration threshold switch (a high-pass mechanism) and a specially designed filtering mechanism. Only acceleration between the upper and lower cut-off thresholds can make the switch transition to its ‘on’ state. Both dynamic finite element simulations and experiments were performed to study the dynamic behavior of the switch. The results indicate that the acceleration band-pass characteristics have been successfully achieved. The upper and lower cut-off thresholds and bandwidth varied with shock duration. The experimental data agreed well with the simulated results. [ABSTRACT FROM AUTHOR]

Subspace learning (SL) plays an essential role in hyperspectral image (HSI) classification since it can provide an effective solution to reduce the redundant information in the image pixels of HSIs. Previous works about SL aim to improve the accuracy of HSI recognition. Using a large number of labeled samples, related methods can train the parameters of the proposed solutions to obtain better representations of HSI pixels. However, the data instances may not be sufficient to learn a precise model for HSI classification in real applications. Moreover, it is well known that it takes much time, labor, and human expertise to label HSI images. To avoid the abovementioned problems, a novel SL method that includes the probability assumption called SL with the conditional random field (SLCRF) is developed. In SLCRF, the 3-D convolutional autoencoder (3DCAE) is first introduced to remove the redundant information in HSI pixels. Besides, the relationships are also constructed using spectral–spatial information among the adjacent pixels. Then, the conditional random field (CRF) framework can be constructed and further embedded into the HSI SL procedure with the semisupervised approach. Through the linearized alternating direction method termed LADMAP, the objective function of SLCRF is optimized using a defined iterative algorithm. The proposed method is comprehensively evaluated using the challenging public HSI data sets. We can achieve state-of-the-art performance using these HSI sets. [ABSTRACT FROM AUTHOR]

With a small number of labeled samples for training, it can save considerable manpower and material resources, especially when the amount of high spatial resolution remote sensing images (HSR-RSIs) increases considerably. However, many deep models face the problem of overfitting when using a small number of labeled samples. This might degrade HSR-RSI retrieval accuracy. Aiming at obtaining more accurate HSR-RSI retrieval performance with small training samples, we develop a deep metric learning approach with generative adversarial network regularization (DML-GANR) for HSR-RSI retrieval. The DML-GANR starts from a high-level feature extraction (HFE) to extract high-level features, which includes convolutional layers and fully connected (FC) layers. Each of the FC layers is constructed by deep metric learning (DML) to maximize the interclass variations and minimize the intraclass variations. The generative adversarial network (GAN) is adopted to mitigate the overfitting problem and validate the qualities of extracted high-level features. DML-GANR is optimized through a customized approach, and the optimal parameters are obtained. The experimental results on the three data sets demonstrate the superior performance of DML-GANR over state-of-the-art techniques in HSR-RSI retrieval. [ABSTRACT FROM AUTHOR]

This communication proposes a method of generating radiation nulls for designing wideband dual-polarized filtering dipole antenna with simple structure. The proposed antenna consists of a pair of crossed dipole radiators, a ground plane, and a balun. Different from the previously proposed methods, the filtering performance of the proposed antenna is achieved by fully exciting the out-of-band resonance modes of the balun and radiator. The proposed dual-polarized filtering antenna has a very simple structure without any extra circuit and nearly has no radiation performance degradation. One radiation null and two other ones are generated at the lower and higher edges of the passband to achieve good frequency selectivity, respectively. The proposed antenna has high efficiency of about 90% inside the operating bandwidth of 1.7–2.8 GHz and out-of-band gain suppression of more than 20 dB. [ABSTRACT FROM AUTHOR]

Classification is an important application of hyperspectral image (HSI). However, it is also a challenging research topic due to the spatial variability of spectral signature and limited training samples. To address these problems, a novel unsupervised feature learning method called latent relationship guided the stacked sparse autoencoder (LRSSAE) is developed in this article, which can effectively exploit the latent relationship under feature space to improve the ability of feature learning. Moreover, the superpixels constraint is employed on the feature representation to avoid the “salt-and-pepper” problem, and it is enforced on the latent relationship to enhance the latent relationship learning additionally. In LRSSAE, combining the stacked sparse autoencoder (SSAE) with the graph regularizations of latent relationship in each hidden layer and the superpixel constraints in the top layer, we extract feature representation in an unsupervised manner. And then, we present a customized iterative algorithm to optimize the LRSSAE. We evaluate the proposed method on three widely used HSI data sets comprehensively. The results demonstrate that our method achieves promising classification performance on these data sets and obtains improvements of 5.06%, 5.77%, and 2.11% in overall accuracy compared to the best SSAE method. [ABSTRACT FROM AUTHOR]

The traditional pedestrian navigation system that uses zero velocity update algorithm cannot calculate traveled distance accurately or observe the heading error. A new model called body odometer (BOR) that consists of a step length model and a correction factor is proposed to obtain precise single step length for dead reckoning. A BOR/MINS integrated framework uses the difference between micro electro mechanical inertial navigation system (MINS) calculated distance and single step length, as a new observation to estimate the correction factor and compensate navigation errors via a Kalman filter. To eliminate the heading error accumulation, a new gyro drift reduction method that combines heuristic drift reduction method and complementary filter is presented. The 200 m straight line experiments show that the calculated distance by BOR/MINS integrated method is much closer to the real distance with the average error percentage of 0.24%. Three differently designed trajectories’ experiments show that the proposed method has a higher match degree with the real trajectories and the positioning error with respect to the total traveled distance is less than 0.6%. [ABSTRACT FROM AUTHOR]

A dual-polarized filtering antenna without using extra parasitic element or filtering circuit is proposed. The proposed antenna consists of two pairs of dipole arms and baluns. The balun feeding scheme is used to form two half-wavelength resonators, which introduce two radiation nulls in the upper stopband. Meanwhile, in the lower stopband, another radiation null is generated by the radiation cancellation between the dipoles and the baluns. As a result, a bandpass filtering response is realized owing to these three radiation nulls. For demonstration, a prototype is fabricated and measured. The prototype has an impedance bandwidth of 50% (return loss >15 dB within the band 1.65−2.75 GHz). The average measured gain is 8.1 dBi. Within the lower stopband of 0−1.25 GHz, the out-of-band suppression level is more than 30 dB. Furthermore, the suppression level is over 16 dB in the upper stopband of 3.3−5 GHz. Such features make the proposed antenna suitable for multiband array applications to reduce the mutual coupling between elements operating at adjacent frequencies. [ABSTRACT FROM AUTHOR]