It is well known that long time coherent integration (LTCI) can effectively improve the radar detection ability of manoeuvring weak targets, since a considerable signal-to-noise ratio (SNR) improvement can be achieved [1]. However, for most existing LTCI algorithms [2-5], there is a common assumption that the observed target is of the single motion stage (i.e., the motion parameters of targets are uniform) during the coherent processing interval (CPI). However, with the advancement of manoeuvrability and the increasement of CPI, the observed target might be of multiple motion stages. In this case, the above-mentioned LTCI algorithms will not be effective any more. The specific LTCI algorithms developed for manoeuvring weak target with multiple motion stages are relatively few. In [6], a short-time generalized radon-Fourier transform (STGRFT) based LTCI algorithm is proposed to remove range migration (RM) and Doppler frequency migration (DFM) effects and estimate the stage-changing point. Similar as GRFT[5], STGRFT can be able to obtain an excellent SNR gain through multi-dimension parametric searching. In [7], a reference signal is introduced to compensate the motion parameters change (MPC) effect between different motion stages, and then GRFT is utilized to achieve the coherent integration during the CPI. However, the computational load of these algorithms is quite high, since the key procedure is based on the multi-dimension parametric searching. This may deteriorate the engineering practicability of these algorithms.

NiOx thin films were prepared on an Au substrate using the electrodeposition (ED), spin-coating (SP), and ED after SP (SP/ED) techniques to realize their application as an electrochemical sensor for the selective detection of trace substances. Results indicated that the electrodeposited films had nanoparticles formed as coarse-grain morphology, and the spin-coated films had a uniform layer with ~60 nm thickness. The thin film prepared by the SP/ED technique showed the highest electrochemical activity, and it was used to record a linear sweep voltammogram to measure the target substance, MSG and glucose, from low concentrations (2 nM) to high concentrations (200 μM). Within the range of concentrations, high R2 values of ≥0.99 were observed for both target substances, confirming that the SP/ED thin films can be used as an electrochemical sensor with high reliability.

Facial expression has been widely used in clinical practice to assess pain in newborns. However, the inherent visual attention required to make such vital inference is poorly understood. It is also unknown whether this inference occurs differently when comparing health professionals with other adults. To investigate these issues, we have recorded and monitored the pupil size signal of 102 subjects (44 experts, 29 parents, and 29 non-experts) while visually analyzing 20 frontal face images of 10 distinct newborns after a painful procedure and at painless rest. Our experimental results have showed that neonatal pain assessment is more cognitively demanding when analyzing the presence of pain rather than its absence. Moreover, our results disclose that a 2-second exposure to a facial expression is sufficient to make this assessment, regardless whether done by health professionals or non-health ones, suggesting that this highly specific visual task is not driven by clinical experience.

Terahertz (THz) imaging has an outstanding advantage of high resolution due to the high frequency and has promising potential in VideoSAR. However, limited to the THz source power and the air absorption, the THz image usually has a low SNR and is susceptible to noise in remote sensing and imaging. In order to improve the quality of THz images, a THz image enhancement method is proposed based on the noise2noise idea. The THz images are reconstructed with the AFBP algorithm. They are organized as noisy image pairs and filtered with a mask to remove the influence of moving targets. Then, the Noise2Noise network is constructed based on the CNN network and takes the noisy image pair as input and reference. In the training stage, 1000 noisy image pairs are used as the training set and 100 noisy images are used as the test set to verify the performance of the proposed method. The experimental results based on real VideoSAR data demonstrate that the proposed method is capable of suppressing noise and enhancing the THz image.

This letter reports a 28-GHz multi-port magneto-electric (ME) dipole array for 5G applications. The proposed antenna array enlarges the system polarization diversity with the capability of being utilized as a balanced antenna, a dual-polarized antenna and a circularly polarized antenna. Co-planar waveguide (CPW) lines are used to connect ME dipole radiators to achieve a high gain with simple feeding structure. The proposed antenna array exhibits a -10dB impedance bandwidth of 12.8% and a maximal peak realized gain of 13.52 dBi as a balanced antenna, and exhibits a -10dB impedance bandwidth of 28.57%, a 3-dB axial ratio bandwidth of 16% and a maximal peak realized gain of 12.15 dBi as a circularly polarized antenna.

A dual-polarized symmetrically cross-slotted square patch (SCSSP) antenna with multimode resonance is proposed for 5G millimeter-wave broadband applications. A symmetrical cross-slot is etched on the square patch surface to change the original E-field distribution where a whole square patch is cut into four disconnected equal parts. For both polarizations, this etching also tunes the resonance frequencies of the two modes to be sufficiently close to each other. Therefore, the antenna can realize good impedance performance within the wide designated bandwidth. Dual-polarized radiation of the SCSSP is vertically excited by three bow-tie-shaped slots and fed by two orthogonal substrate integrated coaxial lines. Simulated and measured results show that the fabricated prototype achieves a broad overlapped impedance bandwidth of 50.0% (24-40 GHz), isolation higher than 30 dB between the two input ports, stable radiation pattern, and low cross-polarization over the operating band. Moreover, the proposed SCSSP antenna with compact size, planar shape, and simple vertical feeding is very well-suited for two-dimensional array design.

A novel microwave limiter with non-reciprocal limiting threshold is proposed in this paper to protect the transceiver switch or the transmitter. The directivity of the directional coupler is utilized to make the power of the received signal input to the detection circuit larger than that of the transmitted signal, thereby the detection circuit provides different DC bias voltage to the limiter circuit and changes the threshold level of the limiter diode. The test results show that this limiter has a threshold level of 35 dBm for the transmitted signal and 17 dBm for the received signal, which has a non-reciprocal limiting threshold for high-power signals input in both directions.

Beamforming technique can effectively improve the spectrum utilization of multi-antenna systems, while the dirty-paper coding (DPC) technique can reduce inter-user interference. In this letter, we aim to maximize the weighted sum-rate under power constraint in a multiple-input-single-output (MISO) system with the DPC. However, the existing methods of beamforming optimization mainly rely on customized iterative algorithms, which have high computational complexity. To address this issue, by utilizing the deep learning technique and the uplink-downlink duality, and carefully exploring the optimal solution structure, we devise a beamforming neural network (BFNNet), which includes a deep neural network module and a signal processing module. Besides, we use the modulus of the channel coefficients as the input of deep neural network, which reduces the input size. Simulation results show that a well-trained BFNNet can achieve near-optimal solutions, while significantly reducing computational complexity

Self-rectifying memristor has no need for selector devices, but possess the one-way transmission behavior and multi-level non-volatile memory characteristics, which makes it promising candidate for electronic synapse. In this letter, we propose a novel self-rectifying memristor based on Pt/Hf0.5Zr0.5O2/TiN structure. The devices show large memory window (104) and high rectifying ratio (104), which can block the sneak current in passive crossbar array without any additional hardware overhead. Moreover, the devices demonstrate excellent multi-level states modulation capability, low power consumption, high endurance and long retention. The final benchmark demonstrates that the proposed Pt/Hf0.5Zr0.5O2/TiN self-rectifying memristor is a promising candidate for electronic synapse application.

A high speed and low power input/output buffer for time interleaving circuit is proposed in this letter. The buffer can be applied to high speed circuits operating at 20GS/s. This novel two-stage buffer is employed with bandwidth expansion and slew-rate enhanced techniques. An improved common-mode feedback circuit stabilizes the output common-mode voltage. This prototype buffer is fabricated in 45nm COMS process, and achieves 7.2bit ENOB at 10GHz input frequency with power consumption of 20.4mW, load of 0.3fF.

This letter studied the phase error in the frequency-interleaving digital-to-analog converter (FI-DAC) and proposed a comprehensive phase estimation method. Firstly, the model of FI-DAC system was established, and the phase error were considered that consists of three parts: time delay error, initial phase error and nonlinear phase error. By analyzing the phase function characteristics of the system, the least squares (LS) was used to linearly fit the phase functions of the non-overlapping bands to estimate both the time delay and initial phase of each sub-band channel. Then, the nonlinear phase error of the system was estimated by the difference between the system phase function and the estimated linear phase function. Finally, the effectiveness of the proposed estimation method was verified by the built FI-DAC test bench.

We have demonstrated waveguide integration of terahertz quantum cascade lasers (THz QCLs) at frequencies above 4.7 THz. A precision micromachining technique, followed by diamond-turning and electroless-plating has been used to manufacture hollow rectangular waveguides with integrated diagonal feedhorns. We show that surface roughness at the 1μm level is achieved, enabling outcoupling of radiation in the 4.75–5.05 THz band, with a divergence angle of 5° along the plane of the QCL substrate.

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.

Deep learning-based classification algorithms have been used for automatic modulation recognition (AMR). However, most methods only focus on end-to-end mapping and neglect the classic key features. In this paper, signals are enforced with key classification features to propose a novel deep learning model for AMR by learning the shared latent space of the aligned signals and key features (LLAF); this is done to increase the generalizability of the model and to ensure the physical plausibility of the results. To obtain adequate signal representations, an encoder-decoder architecture is proposed to learn the shared latent space, and the architecture is trained to approximate prior label distributions for precise signal classification. Simulation results verify the high recognition accuracy of the proposed LLAF model under different signal-to-noise ratios (SNRs).

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Identifying cohesive subgraphs is an important topic in graph theory and complex network analysis. The quasi-clique, as a generalization of clique, can be used to identify functional and structural properties of various networks. In this paper, we study the maximum weighted quasi-clique problem, and propose a local search algorithm for solving the problem. In the algorithm, an iterated local search method is used as the search framework. To find the quasi-clique with the maximum total weights, hybrid vertex selection strategies are proposed and incorporated into our algorithm. The hybrid strategies utilize a probability-based mechanism for choosing sub-strategies in each round of the local search. We conduct experiments on synthetic networks and real-world networks to show the effectiveness of our algorithm. The results indicate that hybrid strategies perform better than existing methods, and thus our algorithm has a good ability to tackle various networks.

In this letter, the issue of mitigating strong co-channel interference (CCI) in communication systems is addressed. Unlike conventional model-based methods, a novel data-driven scheme is proposed. A recurrent neural network (RNN) is trained to directly demodulate the desired signal under strong CCI. Instead of inputting the original received signal, in-phase and quadrature interference-robust features (IRF) are extracted through preprocess. The RNN is then trained offline to implement sequence labelling, with the IRF sequences and known code sequences of the desired signal as inputs and ground-truth labels. Meanwhile, a guard zone is introduced when loading the IRF sequences to enable better contextual information exploitation by the RNN demodulator. Online tests validated the low bit error rate (BER) of the RNN demodulator, under strong CCI. Moreover, the proposed scheme outperformed existing model-based and data-driven interference mitigation schemes in terms of the BER, especially in low signal-to-interference ratio region. Inspiringly, the proposed data-driven scheme generalized well to varied unseen test conditions.

In this letter, we address the challenge in forecasting non-stationary financial time series by proposing a meta-learning based forecasting model equipped with a CNN predictor and a LSTM meta-learner. The model is applied to a set of short subseries which are the result of dividing a long non-stationary financial time series. As a result, a promising performance can be achieved by the proposed model in terms of making more accurate prediction than the traditional CNN predictor and AR based forecasting models in non-stationary conditions.