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.

Genomics data analysis requires efficient tools to address the vast amount of data generated by current next-generation sequencing technologies. K-mer counting works face difficulties in balancing high memory overhead with statistical precision. We designed a high-frequency k-mer statistical computation based on the Space Saving algorithm and a novel hash table structure, which reduces the memory overhead by 46\% while ensuring high computational efficiency.

In this letter, the angular jamming effect of electronically controlled corner reflectors after a maneuver aircraft is analyzed. Motion characteristics of the corner reflectors towed by a high-speed aircraft are calculated through the Lagrange theory, and the angle measurement error of the jamming method for the monopulse angle measurement algorithm is analyzed based on the motion states and the electromagnetic states (scattering or penetration) of the corner reflectors. Results show that the measured angle by a monopulse radar seeker can be effectively interfered by this method under complex maneuver motion while the automatic tracking is employed.

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.

With the increasing penetration of renewable energy, virtual power plants reduce the impact on the power grid by integrating massive distributed resources for unified management. However, the optimal scheduling of a large number of distributed resources in virtual power plants has become a new problem in recent years. Therefore, aiming at the real-time optimal scheduling problem in the optimal scheduling of virtual power plant, this letter regards the virtual power plant as a multi-agent system, and proposes a novel real-time active power dispatch scheme of virtual power plant based on distributed model predictive control, so that each agent can not only calculate its own optimization function relatively independently, but also fully refer to the neighbor information. Simulation results show the feasibility and effectiveness of the proposed method.

The power consumption of chips has emerged as a major concern with the increased integration of analog circuitry. This work focuses on a two-stage comparator based on a preamplifier with latch for successive approximation analog-to-digital converter. In order to minimize power loss and delay time, the charge steering approach was used in the design of latch as well as preamplifier. The suggested comparator is simulated in SMIC 0.18um process in comparison to the comparator without charge steering mode. The results reveal that the average power consumption is only around 22uW for varied input voltage at a supply voltage of 1.2V, which is relatively lowered by approximately 30%. Meanwhile, delay time is also reduced by about 25%.

The motion of targets is well known to result in their defocussing and displacement in SAR imagery, but detection of motion and re-focussing of targets under generic conditions remains of ongoing interest. One class of methods involves forming images of sub-apertures in which motion defocussing will be reduced. In this paper, we use dynamic tomographic image formation methods utilising an optical flow constraint to form a video of SAR sub-aperture images. These retain fine resolution of the full aperture, focussing along-track motion.

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.

This paper proposes a joint optimization method for the imaging algorithm and sampling scheme of sparse spotlight syhthetic aperture radar (SAR) imaging based on deep convolutional neural networks. Traditional compressed sensing (CS) based sparse SAR imaging has been widely studied. Deep learning and sparse unfolding networks have been introduced into sparse SAR imaging, but most current works focus only on the imaging stage and simply adopt the conventional uniform or random down-sampling scheme. Considering that the imaging quality also depends on the sampling pattern besides the imaging algorithm, this paper introduces a learning-based strategy to jointly optimize the sampling scheme and the imaging network parameters of the reconstruction module. In a deep learning-based image reconstruction scheme, joint and continuous optimization of the sampling patterns and convolutional neural network parameters is achieved to improve the image quality. Simulation results based on real SAR image dataset illustrate the effectiveness and superiority of the proposed framework.

We present a low-power area-efficient subarray beamforming receiver (RX) structure for a miniaturized 3-D ultrasound imaging system. Given that the delay-and-sum (DAS) and digitization functions consume most of the area and power in the receiver, the beamforming successive approximation register (SAR) analog-to-digital converter (ADC) shares its capacitive digital-to-analog converter (CDAC) with the delay cells. As a result, the delay cells implemented with capacitors are embedded in the CDAC with significant area reduction, further eliminating the need for power-hungry ADC buffers. Furthermore, the dual reference 10-bit SAR ADC reduces the area of CDAC by 32 times, achieving a switching energy reduction of 98.3%, compared to the conventional SAR ADC. As a result, the proposed beamforming SAR ADC, simulated using a 0.18 μm CMOS process, consumes 230 μW per channel, significantly reducing the per channel capacitance.

This paper shows the comparison between several well-known classification algorithms in Machine Learning with the purpose to find the most suitable algorithm to predict the dwelling time i.e., how long a certain tourist should stay in a particular tourist spot. This dwelling time prediction can be adopted for tour and travel agents to provide optimal scheduling for their package tour. The algorithm in question is strictly for classification because in this case, the dwelling time does not require a very specific number of minutes, thus the time can be classified and restricted into several time frames. The origin and features of the dataset are described in this paper as well as the comparison methodology to show the procedure of how the comparison was made. Lastly, the performance results will be used to determine which algorithm to use for this specific case and it will be shown in a form of a graph

In this letter, the jamming resource allocation problem of distributed jammers cooperatively jamming netted radar system is investigated. A well-constructed jamming resource allocation model considering jamming beams, jamming power and other influencing factors is established. Random keys are used in this letter to improve the coding mode of genetic algorithm. Simulation results show that in the case of limited jamming resources, the model and algorithm proposed can achieve effective jamming allocation schemes facing a netted radar with any number of radar nodes.

Traditional deep convolutional networks (ConvNets) have shown that both RGB and depth are complementary for video action recognition. However, it is difficult to enhance the action recognition accuracy because of the limitation of the single ConvNets to extract the underlying relationship and complementary features between these two kinds of modalities. In this paper, we proposed a novel two stream ConvNet for multi-modality action recognition by joint optimization learning to extract global features from RGB and depth sequences. Specifically, a non-local multi-modality compensation block (NL-MMCB) is introduced to learn the semantic fusion features for the recognition performance. Experimental results on two multi-modality human action datasets, including NTU RGB+D 120 and PKU-MMD dataset, verify the effectiveness of our proposed recognition framework and demonstrate that the proposed NL-MMCB can learn complementary features and enhance the recognition accuracy.

A hydrogen sensing transistor fabricated by a heterojunction bipolar transistor (HBT) with an extended base (EB) formed by a metal-semiconductor-metal (MSM) hydrogen sensor is reported. The power consumption in stand-by mode is smaller than 2 μW. Common-emitter characteristics show that the sensing base (collector) current gains at 25℃ in 0.01%, 0.1%, and 1% H2/N2 are as high as 75 (512), 134, (977), and 233 (2.89  104), respectively. Low-power consumption and high-sensitive gains are indicative that our HBT together with planar-type MSM sensor is very promising for applications to hydrogen sensing transistors using one voltage source.

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.

Affective video content analysis is an active topic in the field of affective computing. In general, affective video content can be depicted by feature vectors of multiple modalities, so it is important to effectively fuse information. In this work, a novel framework is designed to fuse information from multiple stages in a unified manner. In particular, a unified fusion layer is devised to combine output tensors from multiple stages of the proposed neural network. With the unified fusion layer, a bidirectional residual recurrent fusion block is devised to model the information of each modality. Moreover, the proposed method achieves state-of-the-art performances on two challenging datasets, i.e., the accuracy value on the VideoEmotion dataset is 55.8%, and the MSE values on the two domains of EIMT16 are 0.464 and 0.176 respectively. The code of UMFN is available at: https://github.com/yunyi9/UMFN.

Three-dimensional (3D) electromagnetic (EM) parametric modeling and implementation of miniaturized lumped-element power divider (PD) based on symmetrical configuration is investigated. The system design and schematic diagrams of PDs are firstly demonstrated, subsequently, the 3D EM field and field-circuit co-simulation models of PD utilizing computer simulation technology (CST) and advanced design system (ADS) tools are detailedly presented. Finally, one prototype is fabricated and measured. Simultaneously, the gain of the PD is -3.96 dB @ 100 MHz and the experimental curve is in excellent agreement with the simulation. As a conclusion, the novel design methodology applied in PD has important practical engineering value, which can be also applied in millimeter-wave (mmW) circuit.

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.