This letter proposes a polarization microwave correlation imaging method based on the orthogonal complement space. It utilizes the orthogonal complement space of the HV antenna radiation field to cross-multiply the echo information, enabling simultaneous correlation imaging and instantaneous polarization measurement of the target. Currently, there is a significant research gap in polarization-driven microwave correlation imaging methods, and the existing relevant studies focus on enhancing the randomness of the radiation field using polarized antenna elements, without incorporating polarization information into the imaging process. Through simulation analysis, this method further improves the quality of microwave correlation imaging and its ability to resist interference. Moreover, under low time-frequency products, the peak sidelobe level (PSL) and isolation (I) of this method are approximately 3.5 dB and 12.5 dB higher, respectively, than those of traditional instantaneous polarization measurement (TIPM)methods.

The capacitance mismatch problem limits the accuracy improvement of high-precision SAR ADCs (Successive Approximation Register Analog-to-Digital Converters). To address the capacitance array mismatch in SAR ADCs, this paper proposes a novel capacitor calibration scheme based on the Time-to-Digital Converter (TDC). This scheme achieves calibration accuracy as high as 0.01\% and can be flexibly designed to meet the accuracy requirements of SAR ADCs. Simulation results indicate that the capacitance mismatch issue of a redundant capacitor 13-bit SAR ADC can be completely eliminated, and the effective number of bits (ENOB) can approach the ideal value of 13.18 bits. Additionally, the analog component of this scheme utilizes four inverter chains, two D flip-flops, and four counters, without requiring a large area for auxiliary calibration capacitors.

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.

Secure and reliable electricity supply is a prerequisite for the development of smart cities, and the trustworthy and efficient transmission of electrical data is the foundation for the safe and stable operation of the power grid. This paper introduces a real-time data transmission blockchain technique based on parallel proof of work algorithm. The new block generation progress of proposed blockchain is divided into five subroutines: hash pointer computation, real-time data pudding, signature value iteration, interruption, block header assembly. The real-time data pudding and signature value iteration are parallel processed, which brings the effect of decreasing energy loss of blockchain system, and upgrades the speed of new block generation and the bandwidth of data storing on blockchain. Computer simulation shows the proposed strategy can be effectively applied in real-time electrical data transmission application, raising the data transmission reliability with no harm to real-time data transfer function. This strategy provides a solution to guarantee data transmission safety in the digital conversion of power grid.

In order to ensure the power safety of important power customers, a new evaluation of power consumption status of important power customers based on the AHP(Analytic Hierarchy Process)-TOPSIS(Technique for Order Preference by Similarity to an Ideal Solution) algorithm is proposed by fully mining and applying the power big data. Firstly, a power consumption big data analysis platform based on the Hadoop architecture is built to provide a high-performance platform support for big data analysis. Secondly, nine evaluation indexes are constructed from the three dimensions of voltage, load and synthesis, which objectively and scientifically describes the power consumption status of important power customers. Finally, the AHP-TOPSIS algorithm is used to evaluate and analyze the voltage, load and comprehensive indicators respectively, thus, obtaining the evaluation values of three kinds of indicators. The power consumption status scores of important power customers are determined by the variable weight weighted summation. The rationality and feasibility of the method and algorithm are proved by example analysis and field verification. This method helps to promote the transformation from post fault emergency repair to warning beforehand. It has the multiple effects of ensuring safe power consumption, supporting accurate patrolling and active emergency repair serving.

As renewable power generation increases in distribution networks, the real-time power balance is becoming a tough challenge. Unlike simple peak-load shedding or demand turn-down scenarios, generation following requires persistent and precise control due to the temporal response performance of controlled resources. This motivates a comprehensive control design considering the temporal response limitations and execution performance of ACCs when providing such services. Accordingly, this paper proposes a self-constraint MPC that properly allocates the generation following task among different ACCs, consisting of three main parts: response rehearsal, distributed consistency-based power allocation, and real-time task execution. Specifically, the rehearsal knowledge of ACCs is evaluated by introducing model predictive control to track power signals with different values and thus obtain prior factors, including the upward/downward limits and control cost function. On this basis, the coherence of the incremental response costs of different clusters is achieved by containing the prior factors to model the constraints and cost functions. Once the optimised following signals are obtained, a real-time model predictive controller for generation following task execution is employed. Simulations are conducted to verify the feasibility and effectiveness of the proposed method.

The medical robot systems have the effect of reducing the work load and preventing accidents by supporting task that are difficult for inexperienced medical practitioners. Recently, research on automated blood collection and injection robotic system has been increasing, but work is still necessary such as replacing needle tips, setting tourniquet and fixing the patient's arm. In this letter, we propose an intelligent intravenous injection robot without having to see the medical workers face-to-face. We design a revolving system for automatic needle replacement and an arm holder for vessel fixation. Finally, we experiment positioning the needle at the target point by position control based on mathematical modeling and velocity kinematics.

Grease in the normal operation of the RV reducer has a role that can not be ignored, for the variable working conditions of the RV reducer, the performance of the lubricant changes directly affect its reliable operation. Therefore, the study of the rheological properties of the grease has become the focus of the study of RV reducer performance.In this paper, SK-1A grease is taken as the research object, and its rheological characteristics under wide temperature range working conditions (-20℃~40℃) are investigated through rheological experiments, to analyze the potential influence of SK-1A grease on the performance of RV reducer.In addition, to better study the rheological properties of grease under different working conditions, the Elman neural network model was used to predict the rheological properties of grease based on the rheological experiments of grease, and the results were compared with those of the BP neural network and the RBF neural network.The prediction accuracy of the Elman neural network model was assessed using Mean Absolute Error (MAE) and Mean Absolute Percentage Error (MAPE) in a cross-validation approach.The results show that the viscoelastic properties of SK-1A grease generally show a decreasing trend as the temperature rises in a wide temperature range, and the degree of entanglement of soap fibers decreases more obviously, but its fluidity is more stable.The results of the three neural network prediction models show that the Elman neural network model used for the prediction of grease rheological properties shows high prediction accuracy, and the model can provide a valuable theoretical reference for the accurate prediction of the rheological properties of grease affected by complex multifactor.

In this study, an eye blinking re-identification system was proposed. A fast local binary pattern was used for feature extraction because its grayscale invariance and rotational invariance allow for the effective acquisition of feature information even in the presence of noise. Finally, a recurrent neural network and long short-term memory were used for model training. The results indicated that, compared with the model trained using static data, the models based on dynamic features were less affected by environmental noise in terms of accuracy. In addition, the model trained using the recurrent neural network was highly effective in identifying unenrolled users and achieved high overall accuracy.

This study proposed a matched field source localization method based on tensor decomposition. By considering the advantages of tensors in multidimensional data processing, a three-dimensional tensor signal model of space-time-frequency is constructed, and the signal subspace is estimated using high-order singular value decomposition (HOSVD). The source position is estimated by matching the measured data tensor signal subspace with the replica field tensor signal subspace. The S5 event data of SWellEx-96 is processed by the proposed tensor-based matched-field processing (TMFP). The comparison with the results of conventional matched field processing (MFP) shows that TMFP has a better suppression effect on ambient noise under low SNR and better source localization performance.

A novel stripline diplexer design using frequency dependent couplings to achieve multiple transmission zeros is developed in this paper. The transmission zeros generated by the frequency dependent couplings are flexible and controllable, on the basis of the existing cross-coupled, more transmission zeros are introduced to improve the frequency selection characteristics. Based on this characteristic, we designed a 2.6G Hz diplexer, its transmitting channel filter is 5 order with 4 transmission zeros, and the receiving channel filter is 4 order with 5 transmission zeros. We fabricated and measured it, the synthesis results, simulation results, and the tested results are well matched with each other, which will provide more flexibility in the design of diplexers for wireless communication system.

In this work, we report the first demonstration of an ultraviolet light-emitting diode (LED) with boron-containing multiple quantum wells. Electroluminescence emission from the BAlGaN LED was observed at 350 nm, with higher intensity compared to the AlGaN reference LED. A higher operating voltage compared to the reference LED was also observed which may be attributable to a nanomasking behaviour of boron in (Al)GaN alloys.

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.

This letter presents a sub-6 GHz wideband low noise amplifier (LNA) based on double L-type load network and negative feedback technique. Using the cascode structure combined with the above techniques, a single-stage wideband LNA with high gain and low noise figure (NF) can be realized. Fabricated in 110-nm SOI CMOS technology, the proposed LNA achieves a maximum power gain of 15.2 dB, noise figure (NF) of 1.0–1.56 dB. The 3-dB bandwidth ranges from 3.05–4.55 GHz. The minimum power input at 1dB compression point (IP1dB) is -17.1 dBm. The LNA core area is 0.18 mm2 and dissipates a total power of 11.5 mW from 1.4 V power supply.

Strong clutter seriously affects target-of-interest detection in synthetic aperture radar (SAR) images. This letter proposes an unsupervised target detection method (U-TDM) based on a complex-valued extreme learning machine (CV-ELM), the essence of which is to transform the problem of target detection into a pixel binary classification problem. The SAR image is first divided into several unlabeled patches, and fuzzy c-means (FCM) is used to construct the reference target patch set and the clutter patch set. Based on these two patch sets, CV-ELM is used to classify the neighboring patch of the pixel to be detected. Since the pixel intensity and distribution of target-of-interest and clutter are different, unsupervised pixel classification could be realized without ground-truth through U-TDM. Experimental results on GF-3 data and Sentinel-1 data show the efficiency of the proposed method in target detection with a heterogeneous clutter environment.

In this paper, a wideband cylindrical conformal microstrip antenna array employing a proximity-coupled feeding mechanism with a cavity-backed configuration is designed and fabricated. Compared with other conformal microstrip patch antennas by using linear subarrays assembled piecewise, this design uses Teflon instead of traditional dielectric layers, and makes it possible to process the whole conformal array without splicing, obtaining the freedom in unit size and array radius adjustment except ease of manufacturing and assemblage. Combined with the optimization of the cavity size, an array with 44 elements is obtained which has a bandwidth of 40% from 8 to 12GHz and a gain of 16.4 dB.

Computing in-memory technology is a promising way of solving the “memory wall” problem when processing large-scale data,accelerating data processing, and improving energy efficiency. However, calculations in the analog domain are limited in terms of accuracy and sensitivity to process, voltage, and temperature (PVT) changes. In this study, we proposed a computing in memory multiply-and-accumulate (CIM MAC) circuit which could generate pulses automatically and which endows the time-domain approach with a digital character. The MAC result was reflected by the pulse edge and converted into the final digital output using a dual-edge counter quantization circuit, improving the accuracy of the MAC operation and reducing the difficulty of quantization. The performance of the proposed CIM circuit was evaluated using a 28-nm process. It could achieve 4-bit multiplication without errors with an energy efficiency of 40.34 to 883.37 TOPS/W.

Low-carbon development of integrated energy systems is achieved via the sharing of multiple energy interactions by park-level integrated energy systems (PIESs). However, coordinating profit distribution conflict between complex interactive stakeholders under stochastic scenarios is challenging. Accordingly, this study proposes a novel tri-layer framework that aggregates different game mechanisms to investigate the interactions between PIESs and coupled energy markets. First, a linkage trading mechanism is proposed by integrating carbon emissions trading and green certificate trading , which establishes a coupled electricity-carbon-green certificate market. Consequently, a park aggregation operator acts as an intermediary between PIESs and the coupled market, setting purchase and sale prices to guide unit generation in each PIES using the master-slave game theory. Then, the Nash game theory is applied to realize a cooperative bargaining among PIESs for fair revenue distribution. Further, the impact of uncertain environments has been considered using stochastic scenario methods and the conditional value-at-risk theory. Furthermore, to protect the privacy of each participating agent while improving convergence speed, a differential evolutionary method is combined with analysis target cascading to solve the framework. Finally, the proposed scheduling method is verified using a typical case to optimize conflicting PIES interests in multiple scenarios and realize decarbonization transformation.