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.

In this letter, the colored point cloud quality assessment in Augmented Reality (AR) environment was fully studied through subjective test. Firstly, we present a point cloud dataset, named Point Cloud Quality Dataset-AR (PCQD-AR), including ten reference point clouds and their 90 distorted versions, which were encoded by the reference software of Video-based Point Cloud Compression (V-PCC) under different pairs of geometry and texture quantization parameters. Then, the impact of geometry and texture distortions on perceived quality of point clouds in the AR environment was discussed in detail. Moreover, we evaluate the performance of existing objective point cloud quality assessment metrics on the proposed dataset. The subjective dataset including the values of Mean Opinion Score (MOS) will be released after acceptance.

With the increasing innovation of network communication technology, short burst communication has been developed to a certain extent in the time compression technology, which brings great challenges to the radio signal blind reception technology .In the blind reception of non-cooperative signals in short-time burst communication, signal time domain detection is the premise and key of signal modulation parameter estimation, signal demodulation, decoding, interpretation, information acquisition and interference guidance. In this paper, the blind demodulation method of short-time burst FM-MFSK signal is studied under the condition of low signal-to-noise ratio. A short-time burst FM-MFSK blind demodulation method based on STFT is proposed. When SNR is higher than 2dB, the correct demodulation probability for FM-MFSK signal can reach 90%.

The distributed and privacy-preserving characteristics of fine-grained smart grid data hinder data sharing, making federated learning an attractive approach for collaborative training among data owners with similar load patterns. However, malicious models can interfere with training in the federated learning aggregation process, making it difficult to ensure the accuracy and safety of the central model in load forecasting. Therefore, we propose a secure aggregation federated learning method for distributed load forecasting based on similarity and distance (Fed-SAD), which effectively eliminates the interference of malicious models by securely aggregating models, thereby ensuring accurate and safe distributed scenario prediction. Experimental results demonstrate that Fed-SAD maintains high accuracy and robustness in both the presence and absence of malicious models, while maintaining data and model security.

Automatic Modulation Recognition (AMR) is a fundamental research topic in the field of signal processing and wireless communication, which has widespread applications in cognitive radio, non-collaborative communication, etc. However, current AMR methods are mostly based on unimodal inputs, which suffer from incomplete information and local optimization. In this paper, we focus on the modality utilization in AMR. The proxy experiments show that different modalities achieve a similar recognition effect in most scenarios, while the personalities of different inputs are complementary to each other for particular modulations. Therefore, we mine the universal and complementary characteristics of the modality data in the domain-agnostic and domain-specific aspects, yielding the Universal and Complementary subspaces accordingly (dubbed as UCNet). To facilitate the subspace construction, we propose universal and complementary losses accordingly, where the former minimizes the heterogeneous feature gap by an adversarial constraint and the latter consists of an orthogonal constraint between universal and complementary features. The extensive experiments on the RadioML2016.10A dataset demonstrate the effectiveness of UCNet, which has achieved the highest recognition accuracy of 93.2% at 10 dB, and the average accuracy is 92.6% at high SNR greater than zero.

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.

The knife-edge diffraction model (KED) and the uniform geometrical theory of diffraction (UTD) have been widely used to predict the shadowing effect at millimetre-wave (mmWave) bands. This letter proposes a mathematical derivation to rigorously prove that, for an absorbing screen, UTD applying the narrow-angle Fresnel approximation is equivalent to KED. The simulation scenarios are designed to validate the proposal by comparing KED with UTD in the narrow-angle (less than 20○) and wide-angle (over 20○) regions at mmWave bands (20 GHz - 100 GHz). Simulated results agree with the proposal that KED is identical to UTD with a low error of less than 0.1 dB in the narrow-angle region, while they have a difference with an error of over 1 dB in the wide-angle region. In addition, the average computational time is measured and results in both UTD and KED taking approximately 8.0 ms for one test. From the proposal, it can be theoretically explained the differences and similarities between KED and UTD for an absorbing screen.