Automatic dependent surveillance-broadcast (ADS-B) has been widely used due to its low cost and high precision. The deep learning methods for ADS-B signal classification have achieved a high performance. However, recent studies have shown that deep learning networks are very sensitive and vulnerable to small noise. We propose an ADS-B signal poisoning method based on U-Net. This method can generate poisoned signals. We assign one of ADS-B signal classification networks as the attacked network and another one as the protected network. When poisoned signals are fed into these two well-performed classification networks, the poisoned signal will recognized incorrectly by the attacked network while classified correctly by the protected network. We further propose an Attack-Protect-Similar loss to achieve “triple-win” in leading attacked network poor performance, protected network well performance and the poisoned signals similar to unpoisoned signals. Experimental results show attacked network classifies poisoned signals with a 1.55% classification accuracy, while the protected network classifies rate is still maintained at 99.38%.

One of the major bottlenecks of the traditional Current Transformer Energy Harvester (CTEH) is the instable output induced by the wide-range variations of the current in transmission lines. In this work, a novel CTEH capable of generating a stable output is demonstrated by using a core fabricated with saturable magnetic material. The stable output of the CTEH is enabled by the constant voltage-time product in its saturable characteristic. The proposed CTEH is implemented with a resistive load representing the load of electronic devices. When the current in the primary side of the CTEH’s increases from 1A to 1000A, the maximum power on the load can reach about 0.5W, demonstrating the feasibility of using the CTEH with the saturable magnetic core as a quasi-stable power supply.

Considering light absorbing and scattering problems in connection with wavelength can decrease the visibility, contrast and color distortion of images, we propose a new type of convolutional neural network with two training phases. Firstly, the coordinate attention module is integrated into the residual block of the residual group in the backbone network, which is used to strengthen the feature extraction capability of the network. Secondly, since the unrealistic image colors may degrade the image details, an unsupervised method that combines the physical prior knowledge and the real underwater images is proposed to finetune the backbone network. Furthermore, a model protection mechanism is designed to guarantee the successful execution of the training. The experimental results indicate the proposed model can effectively optimize the contrast, color and image quality of the underwater image. Compared with relevant algorithms, our UCIQE and NIQE are respectively 0.525 and 4.149, which further verifies the superiority of the proposed model.

The target and sea clutter Doppler domains frequently overlap due to the frequent passage of slow ship targets through the sea clutter zone. In this letter, a novel sea clutter suppression method is suggested as a solution to this issue, whose key is a novel singular value zeroing criterion guided by the search results of two-dimensional spectral peaks. Verified by simulations, the method proposed can improve signal-to-clutter ratio (SCR) from -8 dB to 41 dB in the frequency domain and be more effective than the conventional SVD-FRFT method in[7] and improved SVD-FRFT method in[8] .

This study investigates the development of InAs quantum dot (QD) lasers on a InP(001) substrate, utilizing only III-arsenide layers. This approach avoids the issues associated with the use of phosphorus compounds, which are evident in the crystal growth of conventional C/L-band QD lasers, making the manufacturing process safer, simpler, and more cost-effective. The threshold current density of the fabricated QD laser was 633 A/cm2, which is the lowest value for QD lasers in the 1.6 μm-wavelength region. This result suggests a high cost-effectiveness and paved the way toward a large-scale production technology for high-performing C/L/U-band QD lasers.

In this letter, a novel model for broadband power amplifier (PA) linearization is proposed, namely Attention Mechanism based Bidirectional Long Short-term Memory network (AM-BiLSTM). In order to verify the linearization performance of the AM-BiLSTM model, a 100MHz bandwidth 5G new radio (5G NR) signal is employed to test the sub-6G PA operating at 2.6-GHz. The experimental results show that the adjacent channel power ratio (ACPR) of the PA with AM-BiLSTM can be improved by 24dB which is 6-dB better than the generalized memory polynomial (GMP) and 3-dB better than the Chebyshev polynomials LSTM (CP-LSTM) in ref[1]. Therefore, the proposed AM-BiLSTM is very effective for the linearization of broadband PA.

In this letter, we introduce a design of virtual guarded SiPMs fabricated in a standard 0.35 μm standard complementary metal oxide semiconductor (CMOS) process. We compare the performance of these virtual guarded cells (VGC) to that of conventional cells with real guard rings, referred to as physical guarded cells (PGC). Specifically, we evaluate the photon detection efficiency (PDE) of both types of SiPMs. Our results demonstrate that the VGC SiPM outperforms the PGC SiPM, exhibiting a true PDE of (22.5 ± 0.5) %, which is significantly higher than the PDE of (10.9 ± 0.3) % obtained for the PGC SiPM. The superior PDE of the VGC SiPM is attributed to a larger active or photosensitive area due to the virtual guard rings and a thinner n-layer in the photosensitive region.

In the process of identifying non-line-of-sight (NLOS), acoustics-based indoor positioning needs to collect audio recordings of sound fields in multiple rooms and upload them to the central server for training. Once the transmission process and server-side suffer malicious attacks, private data will also be leaked. To solve the training difficulty and privacy issues at the same time, we propose a novel Personalized Federated Learning (PFL) model combined with user frequency and room data capacity, taking into account the significant differences in positioning data with room layout. The proposed model can accurately identify the differences between different room data when aggregating on the server-side. By collecting data in the actual indoor environment and comparing the existing algorithms, the accuracy of the proposed method in the data verification of unfamiliar rooms is 90%.

The fusion of multiple monitoring sensors is crucial to improve the accuracy and robustness of machinery fault diagnosis. However, existing fault diagnosis methods may underestimate the interference of noise in the multi-sensor fusion process, leading to unsatisfied performance. To handle this problem, this paper proposes a deep model based on the frequency adaptive wavelet pyramid. First, an adaptive frequency selection strategy is designed to prune the seriously polluted frequencies and only retain some key frequencies. Then, the self-attention mechanism is used to perform information fusion on the selected frequency bands of different sensors. Finally, a wavelet fusion pyramid is adopted by repeating the fusion process at multiple wavelet decomposition levels. In this way, different sensors can be fused in a more fine-grained manner. The experimental results on two multi-sensor-based fault diagnosis datasets demonstrate the anti-noise capability of our proposed method.

An improved coot optimization algorithm is proposed for wireless sensor networks (WSNs) coverage optimization. To monitor the interest field and obtain the valid data, a wireless sensor network coverage model is established. The population is initialized with cubic map and opposition-based learning strategy. The leader population is reversely learned dimension by dimension, so as to improve the diversity of the population and the global optimization ability of the algorithm. The simplex method is introduced to optimize the local exploration of the population. The experimental results show that the enhanced coot optimization algorithm for coverage optimization in wireless sensor networks can reduce energy consumption and improve network coverage.

Rydberg-atom electrometers have the remarkable advantages of self-calibration and high sensitivity. Based on the classical electromagnetic theory, a localized electric field enhancement structure of a hybrid rectangular resonator is proposed to improve the sensitivity of quantum microwave measurement. It should be noted that the prototype of the hybrid rectangular resonator is fabricated and measured at 9.925 GHz. The results of full-wave simulations show that the uniform and high electric field enhancement in the TE101 fundamental mode is realized. The transient process of resonance is simultaneously simulated, and the time to settle steady state is given as about 104 ns. As indicated through experimental results that the structure can reach 24 dB (enhancement factor of 15.8). As a result, the method proposed in this study, based on atomic measurement capabilities, enables us to improve the measurement sensitivity further and promotes the practical development of quantum microwave measurement technology.

Consider general minimum variance distortionless response (MVDR) robust adaptive beamforming problems based on the optimal estimation for both the desired signal steering vector and the interference-plus-noise covariance (INC) matrix. The optimal robust adaptive beamformer design problem is an array output power maximization problem, subject to three constraints on the steering vector, namely, a (convex or nonconvex) quadratic constraint ensuring that the direction-of-arrival (DOA) of the desired signal is separated from the DOA region of all linear combinations of the interference steering vectors, a double-sided norm constraint, and a similarity constraint; as well as a ball constraint on the INC matrix, which is centered at a given data sample covariance matrix. To tackle the nonconvex problem, a new tightened semidefinite relaxation (SDR) approach is proposed to output a globally optimal solution; otherwise, a sequential convex approximation (SCA) method is established to return a locally optimal solution. The simulation results show that the MVDR robust adaptive beamformers based on the optimal estimation for the steering vector and the INC matrix have better performance (in terms of, e.g., the array output signal-to-interference-plus-noise ratio) than the existing MVDR robust adaptive beamformers by the steering vector estimation only.

In the real environment, the unstable radar measurement noise can degrade the tracking performance of the maneuvering target. In this Letter, the iterative formulation of the noise is simplified, and the noise-adaptive matrix is introduced to calculate the fading factor of the strong tracking algorithm, so that the effect of measurement noise on the fading factor can be corrected in real time. The superior performance of the proposed method is verified by comparison with three existing improved methods on a typical example.

Synthetic aperture sonar image reconstruction relies on the coherence of overlapping phase centers to provide accurate micronavigation for a sensed scene. It is shown that phase centers lose coherence for near-range scattering from large SAS arrays due to the fundamentally bistatic nature of these sensors. This effect is modeled using the van Cittert-Zernike theorem and a point-based sonar scattering model. Reduction of the window length used in the delay estimation process can partially mitigate the loss of coherence at the expense of increased variance in the resulting delay estimates.

In this paper, a 2-bit digital reflection-type phase shifter working at 120 GHz is presented. It uses a compact coupled-lines coupler with low insertion loss and high isolation over a wide bandwidth. The loads are made by a microstrip-line loaded by three PIN diodes whose states are tuned ON/OFF to obtain 90°, 180° and 270° phase shift relative to the reference (0°). Measurement results show RMS phase and amplitude error equal to 10.3° and 1.2 dB, respectively. The maximum insertion loss is equal to 8.6 dB, leading to a figure of merit of 31°/dB. As shown by simulation, by flipping PIN diodes and use negative voltage for biasing, the maximum insertion loss could be reduced to 3.6 dB (figure of merit of 75°/dB) along with a great improvement in RMS phase and amplitude errors, thus showing the potential of the proposed architecture.

An ultra-wideband (UWB) antenna with dual band-notched characteristics is proposed in this letter. The fabricated prototype of the proposed antenna has a compact size of 30×24mm which operates at 3GHz to 11GHz and attained dual notch-bands at 3.3-3.7GHz and 5.15-5.825GHz. The result shows that the proposed antenna is compact, easy to fabricate and provides good characteristics in radiation patterns and time-domain behaviors. The simulated and measured results displayed good agreement over the entire operating frequency band. The proposed antenna can be used for wideband frequency requirement systems like indoor positioning.

The accurate prediction of radio wave propagation is extremely important for wireless network planning and optimization. However, inexact matching between the traditional empirical model and actual propagation environments, as well as the insufficiency of the sample data required for training a deep learning model, lead to unsatisfactory prediction results. Our paper proposes a field strength prediction model based on a deep neural network that is aimed at a tiny dataset composed of the geographic information and corresponding satellite images of a target area. This model connects two pretrained networks to minimize the parameters to be learned. Simultaneously, we construct a convolutional neural network (CNN) model for comparison based on a previous advanced study in this field. Experimental results show that the proposed model can obtain the same accuracy as that of previously developed CNN models while requiring less data.

Optical and acoustic stereo imaging has great potential for the precise and consistent localization of intervention underwater robots; however, it is still being explored due to its sensing limitations and various technical challenges. This study presents a novel localization method by combining an inertial navigation system and an optical and acoustic stereo imaging system. As a strategy for localization correction relative to underwater structures, the robot’s pose is estimated based on a single acoustic image using a sonar simulator for mid-range localization, and a robust visual tracking using a 3-D wireframe model is employed for high-precision localization near the target structures. The performance of the proposed technique was demonstrated through experimental validation using real data obtained from a test tank.