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.

Interrupted sampling repeater jamming (ISRJ) is a novel intra-pulse coherent jamming based on digital radio frequency memory (DRFM). By repeatedly sampling and retransmitting the radar transmitting signal fragments, a series of false targets can be formed after pulse compression (PC), posing a severe threat to modern radar systems. Inspired by the energy function method and histogram analysis in image processing, an adaptive time-frequency (TF) filtering method is proposed in this letter. The ISRJ-contaminated regions can be accurately determined in the TF image after histogram analysis and subsequent energy accumulation. Guided by the energy function, the proposed method can automatically adjust the intensity threshold in TF image histogram analysis and therefore reveals better robustness compared with other competing methods. Simulations have verified the effectiveness and robustness of the proposed method against ISRJ under various circumstances.

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.

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.

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).

We demonstrate a high-speed 8.5 μm quantum cascade laser with room temperature continuous wave operation. The maximum output power of 141 mW is obtained at 20 ℃. The parasitic capacitance of the device is decreased from 36.6 pF to 7.1 pF by monolithic integrating a π-shape metal contact electrode. This results in an increase in the -3 dB RF modulation bandwidth from 870 MHz to 4.5 GHz compared with the conventional electrode configuration.

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.

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

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The improvement of the performance of a distributed Bragg reflector laser bar emitting near 905 nm through the use of multiple epitaxially stacked active regions and tunnel junctions is reported. The bar consisting of 48 emitters (each having an aperture of 50 μm) emits an optical power of 2.2 kW in 8 ns long pulses at an injection current of 1.1 kA. This corresponds to an almost threefold increase of the pulse power compared to a bar with lasers having only a single active region. Due to the integrated surface Bragg grating, the bar exhibits a narrow spectral bandwidth of about 0.3 nm and a thermal tuning of only 68 pm/K.

In this letter, a novel sext-band bandpass filter (BPF) using two split-type multiple-mode resonators is proposed. The filter is composed of two sets of resonators which are respectively designed to realize Filter 1 with two passbands and Filter 2 with quad passbands. Then the two BPFs are combined by parallel coupling feed lines for sext-band responses, and each of the six centre frequencies in the proposed sext-band BPF is able to be controlled due to the design freedom. To validate the design and analysis, a prototype filter has been fabricated with six passbands centered at 1.88/2.59/3.48/5.26/5.82/6.75 GHz. The measured result of the fabricated filter agrees well with the simulation, which shows that the proposed structure is a good candidate for sext-band BPF designs and validates the proposed design flow well.

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.

An ultra-low profile and high-performance UHF RFID reader antenna is proposed in this letter, which can be switched between far-field (FF) and near-field (NF) operative mode. This antenna is composed of four dipoles and a reconfigurable feed network. The four dipoles form a square, and the feed network is located in the center. The feed network is a four-way power divider, and the phase of the output signal can be controlled by switching diodes. As a balanced structure, double-sided parallel-strip lines (DSPSLs) are used to feed four dipoles. Compared to the balun structure, the application of DSPSLs can reduce the area of the feed network, thereby reducing the influence of the feed network on the central magnetic field. Experimental results show that the proposed antenna can provide a NF reading area of 180×180 mm2, and the identification rate can reach 100% within 30-60 mm height. The FF gain of the antenna is 4.7 dBic, and the overlapping range of 3 dB AR bandwidth and -10 dB impedance bandwidth is 800-960 MHz. The good FF and NF performance of this antenna is conducive to its application in RFID systems.

A novel frequency-to-voltage converter (FVC) based phase-locked loop (PLL) is proposed to overcome the inability of an FVC-based frequency-locked loop (FLL) to lock phase. The proposed dual-loop PLL adds variable phase-locking capability, such that the phase locking angle can vary from 0o – 360o. The additional variable phase-locking can be applied in data communication in the form of phase modulation. The design is targeted for a 0.5-µm CMOS process. The proposed design generates a 480MHz clock from a reference clock of 15MHz. In simulation, the proposed PLL locks within 3.56 µs while consuming 1.61 mW of power.

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%.

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.

Radio frequency (RF) fingerprinting is a challenging and important technique in individual identification of wireless devices. Recent work has used deep learning-based classifiers on ADS-B signal without missing aircraft ID information. However, traditional methods are difficult to obtain well performance accuracy for classical deep learning methods to recognize RF signals. This letter proposes a Gaussian Low-pass Channel Attention Convolution Network (GLCA-Net), where a Gaussian Low-pass Channel Attention module (GLCAM) is designed to extract fingerprint features with low frequency. Particularly, in GLCAM, we design a Frequency-Convolutional Global Average Pooling (F-ConvGAP) module to help channel attention mechanism learn channel weights in frequency domain. Experimental results on the datasets of large-scale real-world ADS-B signals show that our method can achieve an accuracy of 92.08%, which is 6.21% higher than Convolutional Neural Networks.