The inherent complexity of Wi-Fi signals makes video-aided Wi-Fi 3D pose estimation difficult. The challenges include the limited generalizability of the task across diverse environments, its significant signal heterogeneity, and its inadequate ability to analyze local and geometric information. To overcome these challenges, we introduce WiViPose, a video-aided Wi-Fi framework for 3D pose estimation, which attains enhanced cross-environment generalization through cross-layer optimization. Bilinear temporal-spectral fusion (BTSF) is initially used to fuse the time-domain and frequency-domain features derived from Wi-Fi. Video features are derived from a multiresolution convolutional pose machine and enhanced by local self-attention. Cross-modality data fusion is facilitated through an attention-based transformer, with the process further refined under a supervisory mechanism. WiViPose demonstrates effectiveness by achieving an average percentage of correct keypoints (PCK)@50 of 91.01% across three typical indoor environments.

Although remote sensing image registration has been developing at a high speed for decades, polarimetric synthetic aperture radar (PolSAR) image registration is still a challenging task because of the presence of polarimetric scattering differences, geometric distortions, and speckle noise. Due to the lack of PolSAR image training data, the generalization performance of deep learning-based registration methods is poor and cannot fundamentally solve the problem of PolSAR image registration. In this article, we propose a novel PolSAR image registration framework that integrates feature selection, feature descriptor extraction, and template matching. First, we use structural similarity (SSIM) to select polarimetric features that are similar in structural information, thus using structural information to overcome polarimetric scattering differences. On this basis, a feature descriptor named oriented gradient of polarimetric feature (OGPF) is proposed to overcome the polarimetric scattering information difference by extracting geometric structure information using oriented gradient channels (OGCs) and 3-D Gaussian convolution. Finally, we propose to use polarimetric whitening filter (PWF) and nonmaximum suppression (NMS) to extract keypoints with significant structural information to reduce the interference of speckle noise on keypoint selection, and further propose a template downsampling strategy to reduce the complexity of template matching. The proposed method is evaluated using six pairs of PolSAR images with different scenes, and the results show that its registration performance outperforms the state-of-the-art methods.

Programmable switches have been proposed in today’s network to enable flexible reconfiguration of devices and reduce time-to-deployment. Buffer sizing, an important factor for network performance, however, has not received enough attention in programmable network. The state-of-the-art buffer sizing solutions usually employ either fixed buffer size or adjust the buffer size heuristically. Without programmability, they suffer from either massive packet drops or large queueing delay in dynamic environment. In this paper, we propose Adaptive Buffer Sizing (ABS), a low-cost and deploy-friendly framework compatible with programmable network. By decoupling the data plane and control plane, ABS-capable switches only need to react to the actions from controller, optimizing network performance in run-time under dynamic traffic. Meanwhile, actions can be programmed by particular Machine Learning (ML) models in the controller to meet different network requirements. In this paper, we address two specific ML models for different scenarios, a reinforcement learning model for relatively stable network with user specific quality requirements, and a supervised learning model for highly dynamic network condition. We implement the ABS framework by integrating the prevalent network simulator NS-2 with ML module. The experiment shows that ABS outperforms state-of-the-art buffer sizing solutions by up to 38.23x under various network environments.

Small target detection is a very challenging problem since a small target contains only a few pixels in size. At present, many deep learning-based detection algorithms for small targets have achieved remarkable results, mainly including improvements in data augmentation, multiscale images, multiscale features, training strategies, and so on. However, these deep learning-based methods cannot select the positive and negative samples for the large-difference-scale targets well in the label assignment operation. The reason is that intersection over union (IoU), which is widely used in the most target detection networks, has great limitations for small target detection. However, in practical applications, there are often some large-difference-scale targets in synthetic aperture radar (SAR) images, especially existing some tiny targets due to the limitation of resolution. To fundamentally break the limitations of IoU, we propose to use the Bhattacharyya distance (BD) instead of the IoU metric to improve the performance of small target detection. We further revise the Bhattacharyya distance (RBD) to better measure the deviation of bounding boxes for targets with large differences in size. RBD can embed anchor-based detectors to replace the IoU metric in label assignment and nonmaximum suppression (NMS). The proposed method is evaluated on the LS-SSDD-v1.0 dataset and the experimental results show that the proposed method outperforms the state-of-the-art methods.

Currently, deep neural networks have been widely used in the field of SAR target recognition. Many researchers found that deep neural networks have an ability of denoising. In many cases, there is no need to denoise in pre-process. But the denoising ability of deep neural networks can take place of conventional denoising algorithm or not is doubtful. In this article, we explore the effect of image denoising algorithms to SAR target recognition methods based on deep neural networks. Firstly, seven traditional denoising algorithms are selected to process two SAR datasets. And these data are utilized to train two kinds of deep neural networks. After comparing and analyzing the training processes and results, we find that 1) The effect of denoising algorithms is influenced by architectures of neural networks and quality of datasets. It is difficult to find a SAR image denoising algorithm, which can improve the accuracy of any recognition network. Sometimes they even drag down the performance of recognition networks. 2) The deep networks with more layers will have better denoising ability, so the effect of denoising algorithms will decrease. For ResNet, there is no need to add the denoising processing.

Today, Internet service deployment is typically implemented with server replication at multiple locations for the purpose of load balancing, failure tolerance, and user experience optimization. Domain name system (DNS) is responsible for translating human-readable domain names into network-routable IP addresses. When multiple replicas exist, upon the arrival of a query, DNS selects one replica and responds with its IP address. Thus, the delay caused by the process of DNS query including the selection of replica is part of the connection setup latency.

In this paper, we improve the Siamese Networks for SAR target few-shot learning. SAR target recognition is an important branch of SAR application. It can efficiently extract target category information from complex SAR images and help humans quickly understand SAR images. However, many successful machine learning methods require large amounts of annotated data. So, few-shot learning is always a topical challenge for machine learning. We apply Siamese Networks to SAR target recognition with limited data and improved it. Our model consists of CNN encoder, similarity discriminator and classifier. Relevantly, it has two inputs and three outputs. CNN encoder is constrained by similarity discriminator and classifier. Furthermore, the larger difference from the Siamese Network is that the target category is outputted by the classifier, not by the similarity discriminator. Our method not only makes use of the advantage of metric learning to improve the accuracy of SAR target recognition with limited data, but also significantly reduces the prediction time consumption for the model based on metric learning. In the ten categories military vehicle classification task, there are only five samples for each category and a total of 2425 testing samples. Our method outperforms A-ConvNet and Siamese Networks by 15.8% and 8.41%. The prediction time consumption of Siamese Networks is 114.832s, while that of our method is 1.172s.

SAR/INS navigation system utilizes SAR radar as an additional sensor to provide the platform trajectory position and compensate an aircraft drift due to Inertial Measurement Unit(IMU) errors. Images obtained by SAR radar are matched with a digital landmark Data. When no landmarks are available, this article presents a novel method of InSAR/INS integrated navigation system based on interferogram matching. Interferogram contains total terrain information and hardly affected by seasonal variations of features compared with SAR/INS system. Interferogram from actual InSAR real time processing and interferogram simulated by additional DEM are matched and platform position and attitude inversion model is constructed to compensate INS drift errors. Real-data experiments show that the approach can compensate INS drift errors well when GPS signal is lost.