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.

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.

In this letter, we focus on the problem of direct position determination (DPD) for moving targets. Compared with the traditional two-step localization methods, the DPD methods is more robust at low signal-to-noise ratio (SNR). However, to guarantee the optimal location results the computational complexity of DPD with grid search is too high, especially for moving targets. Therefore, we propose a new DPD method for moving targets with low computational complexity. First, using a proposed cost function, we obtain the position information from the received array signals directly. Second, we use the method of successional difference which averages the difference result of position over time to extract the velocity. It avoids the multi-dimensional parameters grid search and reduces the the computational complexity greatly. Simulation results demonstrate that the proposed algorithm outperforms other methods in a wide range of scenarios, especially at low SNR.

The line of sight (LoS) probability is a key factor for the channel modeling of air-to-ground (A2G) communication. However, the existing LoS probability models do not account for the effects of airframe shadowing (AS) and building density, which can cause serious link obstruction and performance loss due to the six-dimensional (6D) mobility and self-body of unmanned aerial vehicle (UAV). In this paper, a new LoS probability model is proposed that considers the AS and building density for different UAV heights. Adding to this, the AS is derived in terms of UAV framework and 6D mobility. Next, the machine learning (ML) based graph neural network (GNN) method is developed to learn the features and structure of the urban environment and predict the LoS probability. Then, the GNN model is trained and evaluated based on the ray tracing (RT) data to establish the relationship between model parameters and UAV heights under the building density and AS factors. The interpretation and explanation of the proposed GNN model and prediction are also discussed in this paper. It is shown from the simulation analysis that the GNN model accurately captures the effects of AS, building height distributions, and UAV heights, with high accuracy compared to the baseline 3GPP, GCM and NYU models.

This paper presents a prediction-based dynamic range (DR) compensation technique in continuous-time (CT) delta -sigma modulator (DSM) with digital-domain noise coupling (DNC) structure. A digital prediction technique is proposed to address the issue of maximum stable amplitude (MSA) loss caused by DNC. The proposed prediction method has been shown to significantly extend the MSA of CT DSM with DNC. Moreover, through the extension of MSA, the DR and SNDR is significantly improved. A CTDSM with proposed compensation achieves a SNDR of 85.1dB at -1dBFS and a DR of 89dB.

Future communication networks are widely considered to be able to provide both high-speed communication service and reliable sensing service. Integrated sensing and communication (ISAC) has been a novel trend due to its possible hardware gain and spectrum gain by the integration of signal communication and sensing capabilities. Appropriate resource allocation strategy is essential for ensuring the quality of both communication and sensing services in terms of ISAC. Existing ISAC-based resource allocation schemes mainly focus on the coexistence of sensing and communication. However, in multi-user ISAC networks, the services required by users are diverse, including sensing-only, communication-only and dual-function cases. To this end, we proposes a resource allocation strategy based on user quality of service(QoS). Specifically, the sum rate of cellular network is maximized via optimizing spectrum resource selection and transmit power, while meeting the sensing QoS. We achieve the optimal solution to the entire problem by first demonstrating that the communication rate in this paper is a monotonically increasing function of sensing power, allowing us to obtain the optimal power allocation scheme. Subsequently, we employ a matching method to obtain the optimal spectrum sharing scheme.

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.

Indoor navigation networks serve as the foundation for indoor pedestrian navigation services. However, current graph-based topological network models cannot easily represent complex indoor structures in a manner consistent with user behaviour and cognitive patterns, and thus cannot support fine-grained indoor navigation analysis services. To enhance the rationality of the model, we considered the influences of morphological and functional characteristics of indoor spaces on pedestrian movement and proposed a method for classifying navigation spaces. This method, based on convex space segmentation, defines morphological and functional characteristic functions, classifying indoor navigation spaces as either corridors or open spaces, which suitable for representing as median axis models or visual graphs, respectively. These categories supported the creation of topological network models for route planning analysis. Indoor map experiments confirmed the effectiveness of our method in identifying various types of corridors and open spaces, that addressing the limitations of width-based parameters，even in complex indoor settings.

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.

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.

Semantic communication has attracted significant attention as a key technology for emerging 6G communications. Though it has lots of potentials specially for high volume media communications, still there is no proper quality metric for modelling the semantic noise in semantic communications. This paper proposes an autoencoder based image quality metric to quantify the semantic noise. An autoencoder is initially trained with the reference image to generate the encoder decoder model and calculate its latent vector space. Once it is trained, a semantically generated/received image is inserted to the same autoencoder to create the corresponding latent vector space. Finally, both vector spaces are used to define the Euclidean space between two spaces to calculate the Mean Square Error between two vector spaces, which is used to measure the effectiveness of the semantically generated image. Results indicate that the proposed model has a correlation coefficient of 88% with the subjective quality assessment. Furthermore, the proposed model is tested as a metric to evaluate the image quality in conventional image coding. Results indicate that the proposed model can also be used to replace conventional image quality metrics such as PSNR,SSIM,MSSIM,UQI, VIFP, and SSC whereas these conventional metrics completely failed in semantic noise modelling.

Hand pose estimation based on a single RGB image has low accuracy due to the complexity of the pose, local self-similarity of finger features, and occlusion. A multiscale feature fusion network (MS-FF) for monocular vision gesture pose estimation is proposed to address this problem. The network can take full advantage of different channel information to enhance important gesture information, and it can simultaneously extract features from feature maps of different resolutions to obtain as much detailed feature information and deep semantic information as possible. The feature maps are merged to obtain the hand pose results. The InterHand2.6M dataset and Rendered Handpose Dataset (RHD) are used to train the MS-FF. Compared with the other methods (which can estimate interacting hand poses from a single RGB image), the MS-FF obtains the smallest average error of hand joints on RHD, verifying its effectiveness.

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.

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.

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.