In this paper, we introduce an innovative temporal consistency enhancement approach, which enables image-based models on video data by leveraging a deep-learning-based Kalman Filter. More specifically, we propose a novel Bi-direction Kalman filter strategy, utilizing forward and backward processing to capitalize on higher-quality pose estimations near the camera, enhancing the robustness and precision of vehicle tracking across varying distances and conditions. Then, rather than using the conventional mathematical motion model, we propose a learnable motion model, dubbed Future State Predictor, to represent the complex, non-linear motion patterns observed in vehicles. The experimental results demonstrate that our approach enhances pose accuracy and temporal consistency, which allows us to handle the challenging occluded/distant vehicles.

The objective of this paper is to determine the transient characteristics and sites of the pre-breakdown (pre-BD) in the spark conditioning under power frequency voltage (PFV) in vacuum. A multi-angle optical path system was built to record the arc of pre-BD at two viewing angles by one camera, realizing the determination of the pre-BD sites in spark conditioning. The recording rate of high-speed camera was 580,000 frames/s. The experimental results indicate that, in the spark conditioning under PFV, the pre-BDs continuously occurred at a random moment. Within several half-waves, the number of the pre-BDs could reach dozens of times, which was the key for the treatment of the electrode surface in conditioning. Under the application of PFV, the single pre-BD occurred at a random moment, in which would last for several microseconds and the peak value of the current would reach several amperes. Moreover, the pre-BD sites randomly distributed in the surface of the electrode and almost covered the whole surface of the electrode. The number of the pre-BDs in edge region of the electrode tent to be more. The pre-BDs took place independently in both time and space.

Increasing Internet of Things (IoT) deployments present a growing surface over which villainous actors can carry out attacks. This disturbing revelation is amplified by the fact that a majority of IoT devices use weak or no encryption at all. Specific Emitter Identification (SEI) is an approach intended to address this IoT security weakness. This work provides the first Deep Learning (DL) driven SEI approach that upsamples the signals after collection to improve performance while simultaneously reducing the hardware requirements of the IoT devices that collect them. DL-driven upsampling results in superior SEI performance versus two traditional upsampling approaches and a convolutional neural network only approach.

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.

Edge computing has been proved an efficient approach to provisioning computation offloading service to vehicles on road through Road-Side Units (RSUs). However, the traffic volume on road is highly dynamic, while RSU-based edge servers are static in terms of geographical location and computation capacity. To address this problem, this paper proposes a mobile edge server placement strategy using cruising UAVs along the roads based on the genetic algorithm. We first build a mathematical model to characterize the deployment cost of these UAV-mounted servers and their routes. Next, we design a heuristic UAV-mounted edge server deployment scheme based on K-medoid clustering and genetic algorithms. Experimental results verify that our proposed UAV deployment scheme satisfies the offloading demand of IoV nodes while reducing the total deployment cost by 17.05% to 48.94% compared with existing popular approaches.

With the wide application of vision-based autonomous driving and mobile robots, the impact of frequent sand-dust weather on computer vision applications in landlocked countries during spring and autumn has also attracted more and more attention. Although there has been a lot of research on sand-dust image enhancement, no research has been conducted on how to improve the positioning accuracy of vision-based autonomous driving or mobile robots in sand-dust environments, especially because there is currently a lack of data sets to evaluate visual positioning in sand-dust weather. Therefore, we propose a complete set of visual positioning data set construction methods in sand-dust weather to fill the gap in the evaluation data set of application fields such as autonomous driving or mobile robot attitude estimation in sand-dust weather. At the same time, this method is also suitable for the construction of visual positioning data sets under haze and other similar weather. In addition, this paper further demonstrates to readers how to use the converted dust visual positioning data set to conduct positioning evaluation experiment of automatic driving in sand-dust weather.

In this study, a ZnCo2O4/Ag3PO4 composite catalyst was prepared by the precipitation method, and its photocatalytic degradation activity for methyl orange (MO) was studied. The catalysts were characterized by TEM, XRD, EDX, HRTEM, SAED, SEM, XPS, and UV-Vis-DRS. The results indicate that 0.1 ZnCo2O4/Ag3PO4 The composite system has a good photocatalytic degradation effect on methyl orange. Under 30-minute simulated sunlight conditions, the degradation rate can reach 94%. The results show that the maximum reaction rate constant of 0.1 ZnCo2O4/Ag3PO4 is 0.05301 min-1, which is three times the size of pure Ag3PO4 and 52 times the size of pure ZnCo2O4. After three cycles, 0.1 ZnCo2O4/Ag3PO4 still degraded methyl orange (MO) at a rate of 84.4%. The trapping experiment showed that hole (h+) and O2- played the most important roles in the photocatalytic degradation field of methyl orange (MO) by 0.1 ZnCo2O4/Ag3PO4, and hydroxyl radical (OH·) played a partial role. The energy level structure of ZnCo2O4/Ag3PO4 is conducive to the effective separation of photogenerated electrons and holes, improving the lifespan of photogenerated charges. Among them, the photocatalytic performance of 0.1 ZnCo2O4/Ag3PO4 is the most excellent.

The voltage endurance coefficient n of cross-linked polyethylene (XLPE) insulation is an important indicator for the analysis of insulation failure mechanism. Accurately obtaining the value of n can provide technical support for the reliability evaluation of cable operation. The constant stress method and the step stress method are used to obtain the values of n, but the test parameters can influence the accuracy of the obtained n values. Two methods for selecting test parameters are proposed. The equivalence between the two methods is established, providing theoretical support for the accurate evaluation of the n value of solid insulation materials by using the step stress method. The accuracy of the evaluation results is verified by analyzing the test data. The test results show that the nstep=13.04 evaluated by the step stress test with the proposed parameter selection method is consistent with the test result of constant stress nconstant=12.83.

Welcome to this Special Issue of Wiley’s Healthcare Technology Letters (HTL) journal dedicated to the 2023 edition of the Augmented Environments for Computer-Assisted Interventions (AE-CAI), Computer Assisted and Robotic Endoscopy (CARE), and Context-aware Operating Theatres (OR 2.0) joint workshop. We are pleased to present the proceedings of this exciting scientific gathering held in conjunction with the Medical Image Computing and Computer-Assisted Interventions (MICCAI) conference on October 8th, 2023 in Vancouver, British Columbia, Canada. We hope that you will enjoy reading this Special Issue and we look forward to your continuing support and participation in future editions of the AE-CAI, CARE and OR 2.0 workshops. Their continued success demands our ongoing commitment and support, and we hope to welcome you all to the next edition of the workshop at MICCAI 2024 in Marrakesh, Marocco.

Occluded person Re-identification (Re-ID) is to identify a particular person when the person’s body parts are occluded. However, challenges remain in enhancing effective information representation and suppressing background clutter when considering occlusion scenes. In this paper, we propose a novel Attention Map-Driven Network (AMD-Net) for occluded person Re-ID. In AMD-Net, human parsing labels are introduced to supervise the generation of partial attention maps, while we suggest a Spatial-frequency Interaction Module (SIM) to complement the higher-order semantic information from the frequency domain. Furthermore, we propose a Taylor-inspired Feature Filter (TFF) for mitigating background disturbance and extracting fine-grained features. Moreover, we also design a part-soft triplet loss, which is robust to non-discriminative body partial features. Experimental results on Occluded-Duke, Occluded-Reid, Market-1501, and Duke-MTMC datasets show that our method outperforms existing state-of-the-art methods. The code is available at: https://github.com/ISCLab-Bistu/SA-ReID.

For telecom operators, customer service is integral to their business. Traditional service systems, responsible for managing large amounts of data and complex knowledge bases, need more time retrieval processes and a lack of precision, hindering their ability to respond quickly to customer requests. To address these issues, this paper uses the LangChain programming framework to create a customized Large Language Model (LLM) specifically for the customer service context of telecom operators. It also uses reinforcement learning to improve the performance of the models and reduce the production of incorrect information. Experimental results show that the acceptance of our model’s recommended knowledge has increased from 15% to 70%, confirming its reliable operation in resource-constrained environments.

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.

Targets with rotating components generate micro-motion (MM) modulation effect in addition to the main body. Extracting MM parameters is challenging due to interference from the target’s main body, necessitating the separation of modulation signals. This letter proposes a robust complex local mean decomposition (RCLMD) method with self-adaptive sifting stopping, aiming at the problem of component redundancy due to multiple iterations during break and the loss of modulation components during the separation process. The proposed method sets the objective function and self-adaptive stopping criterion, combined with the modulation signal characteristics, enhancing the accuracy and efficiency of MM component extraction. Simulation experiments indicate that at a low signal-to-noise ratio (SNR) of 3 dB, the separation effect of RCLMD is still 14.72\% higher than that of the conventional complex local mean decomposition (CLMD) method, and the separation efficiency is improved by 54.92\%. Furthermore, the measured radar signals verify the effectiveness of the proposed method in real scenarios.

Integrated sensing and communication (ISAC) is a promising technique for unmanned aerial vehicle (UAV)-empowered 6G networks. In this letter, we propose a novel adaptable ISAC mechanism. Firstly, we investigate both the communication and sensing channel model under Doppler and beam-squint effect for the UAV ISAC with the wideband massive multiple-input multiple-output (MIMO) configuration. Then, we propose an adaptive sensing method by the joint designing the signal echo sensing and beam-squint sensing under practical environment. Specifically, when the echo power is larger than the minimum detectable power, the proposed method could realize the effective sensing by utilizing the system model without feedback. Otherwise, the proposed method could exploit the beam-squint for sensing with the feedback of the sub-carrier index. Finally, various simulation results are provided to demonstrate the effectiveness of the proposed scheme.

In the field of compressed sensing, $\ell_{1-2}$-minimization model can recover the sparse signal well. In dealing with the $\ell_{1-2}$-minimization problem, most of the existing literatures use the DCA algorithm to solve the unrestricted $\ell_{1-2}$-minimization model, i.e. model $(\ref{my1})$. Although experiments have proved that the unrestricted $\ell_{1-2}$-minimization model can recover the original sparse signal, the theoretical proof has not been established yet. This paper mainly proves theoretically that the unrestricted $\ell_{1-2}$-minimization model can recover the sparse signal well, and makes an experimental study on the parameter $\lambda$ in the unrestricted minimization model. The experimental results show that increasing the size of parameter $\lambda$ in model $(\ref{my1})$ appropriately can improve the recovery success rate. However, when $\lambda$ is sufficiently large, increasing $\lambda$ will not increase the recovery success rate.

In this letter, an analytical method for the beampattern synthesis of symmetric nonuniform array is proposed. This method consists of two steps. In the first step, it acquires a real symmetric excitation by the convex optimization method to attain a pencil beam. In the second step, it superposes the pencil beams pointing in different directions to synthesize the prescribed beampattern. Numerical results are provided to verify the effectiveness of the proposed method.

The introduction of YOLOv9, the latest version of the You Only Look Once (YOLO) series, has led to its widespread adoption across various scenarios. This paper is the first to apply the YOLOv9 algorithm model to the fracture detection task as computer-assisted diagnosis (CAD) to help radiologists and surgeons to interpret X-ray images. Specifically, this paper trained the model on the GRAZPEDWRI-DX dataset and extended the training set using data augmentation techniques to improve the model performance. Experimental results demonstrate that compared to the mAP 50-95 of the current state-of-the-art (SOTA) model, the YOLOv9 model increased the value from 42.16% to 43.73%, with an improvement of 3.7%. The implementation code is publicly available at https://github.com/RuiyangJu/YOLOv9-Fracture-Detection.

A power line carrier (PLC) communication characteristics-based method is proposed for single-phase-to-ground fault location in neutral isolated medium voltage (MV) meshed distribution networks in this paper. The carrier signals with a time-varying frequency and constant amplitude are processed by a set of PLC transmitters and receivers, whose placement is optimized by regarding the power network as an undirected graph. Two signal encoding and decoding algorithms for the PLC terminals are proposed to avoid using expensive timing systems between the terminals. The fault location technique is implemented by comparing the cosine similarity of amplitude attenuation (AA) and phase offset (PO) between the fault and a library. The node corresponding to the maximum cosine similarity of the characteristics between the present fault and the library is selected as the location of the current fault. Only one set of low-cost PLC communication terminals and the widely available power lines are needed in the fault location system, making this approach highly practical. Numerical simulations using MATLAB/Simulink have been performed to verify the technique’s feasibility. The results show that the method can accurately locate faults in neutral isolated MV meshed distribution networks. Besides, the presented approach achieves high accuracy in estimating transition resistance values.