Traditional magnetic tactile sensors are highly susceptible to external magnetic field interference, limiting their reliability in practical applications. To address this challenge, we propose a dual-modal soft magnetic skin capable of simultaneously acquiring magnetic and force tactile information across spatiotemporal domains, inspired by the sensory mechanisms of human skin. The system integrates a Convolutional Neural Network-Convolutional Neural Network-Multilayer Perceptron (CNN-CNN-MLP) architecture to fuse these dual-modal signals effectively. Furthermore, we introduce a novel Dynamic Weighting Coefficient Layer (DWCL) to dynamically optimize fusion weights for each modality based on real-time input characteristics, thereby enhancing robustness against magnetic interference. The DWCL leverages temporal discrepancies between modalities during pre-contact sensing and quantifies the magnetic field strength of target objects to autonomously adjust fusion ratios, prioritizing the more reliable modality under varying interference conditions. Extensive experimental evaluations demonstrate that the proposed DWCL significantly improves interference resistance compared to conventional fusion methods, advancing the feasibility of magnetic tactile sensing in real-world environments.

Training accurate deep neural networks in the presence of noisy labels is an important task. Though a number of approaches have been proposed for learning with noisy labels, many open issues remain. In this paper, we show that DNN learning with Cross Entropy is not robust to label noise and exhibits imbalance between the gradient of clean and noisy samples. We propose a new loss function, Equal Loss (EL), boosting DNN with a relaxed target probability and balanced gradient density. Both theoretical analysis and experiments on a range of benchmarks and real-world datasets show that EL outperforms state-of-the-art methods.

Binary neural networks (BNNs) contribute a lot to the efficiency of image classification models. However, in dense predication tasks such as human pose estimation, predictions in different locations are coupled and rely on the extraction of features across entire images. As a result, more robust and adaptive binarization is required to bridge the performance gap between binarized and full precision models. We propose two approaches to conduct image-aware and pixel-aware dynamic binarization in a model for human pose estimation. Firstly, a simplified dynamic thresholding is leveraged in the backbone to determine unique binarization thresholds for each image. Secondly, in the decoder, we decouple binarization for each pixel according to the activations surrounding the pixel. Dynamic filtering modules are proposed to determine a different binarization strategy for each pixel. Compared with the strong baselines, the proposed framework improves 5.2% and 3.6% mAP on the COCO test-dev benchmark for ResNet-18/34 architectures respectively.

The energy hub has gained a lot of importance as a way to optimize multicarrier energy infrastructure to enhance its flexibility and proficiency. In the presence of energy storage in energy hubs, it is possible to efficiently coordinate energy supply and demand while taking advantage of changing energy tariffs and local renewable energy supply sources. In this article, we propose a novel method based on gray wolf optimization (GWO), which breaks down the complex optimization problem into subproblems, including the storage scheduling and another part in the grid, and comes up with the respective solutions via GWO and interior point. As a result, the proposed method avoids the drawbacks of numerical ways and artificial intelligence techniques, which tend to converge only to a local minimum or require heavy computational times. Furthermore, the article proposes a multilayer cloud computing framework for optimally scheduling energy hubs. The present study applies the new technique to a two-hub and three-hub system during 24 h. The approach is also examined in the context of an 11-hub system to investigate the efficiency and potential applications. The outcomes prove that the approach can achieve a very near-global point, which is confirmed through an analytical method, as well as being rapid sufficient to permit for online implementations with receding time horizons.

The live streaming is more challenging than on-demand streaming, because the low latency is also a strong requirement in addition to the trade-off between video quality and jitters in playback. To balance several inherently conflicting performance metrics and improve the overall quality of experience (QoE), many adaptation schemes have been proposed. Bitrate adaptation is one of the major solution for video streaming under time-varying network conditions, which works even better combining with some latency control methods, such as adaptive playback rate control and frame dropping. However, it still remains a challenging problem to design an algorithm to combine these adaptation schemes together. To tackle this problem, we propose a hybrid control scheme for adaptive live streaming, namely HYSA, based on heuristic playback rate control, latency-constrained bitrate control and QoE-oriented adaptive frame dropping. The proposed scheme utilizes Kaufman's Adaptive Moving Average (KAMA) to predict segment bitrates for better rate decisions. Extensive simulations demonstrate that HYSA outperforms most of the existing adaptation schemes on overall QoE.

Cloud-based video surveillance requires protecting privacy yet allowing cloud to perform motion detection and tracking. Prior art allows performing surveillance on encrypted videos but details of trajectories of moving objects are exposed. In this paper, we propose a video surveillance system that supports adjustable granularity of motion detection and tracking for fine-grained control on conflicting requirements between privacy protection and accuracy of motion detection and tracking. Our encryption adds a permutation layer to conventional format-compliant selective encryption to ensure motion information can be recovered only at a given granularity yet incurs a very small impact on the bitrate and processing speed of video compression. Our motion detection method estimates both local and global motions to distinguish foreground motions from background motions and deduce trajectories of foreground moving objects. Experimental results indicate that our system has fulfilled the design goal.