Unmanned aerial vehicle (UAV) detection based on thermal infrared imaging has been one of the most important sensing technologies in the anti-UAV system. However, the technical limitations and long-range detection of thermal sensors often lead to acquiring low-resolution (LR) infrared images, thereby bringing great challenges for the subsequent target detection task. In this article, we propose a novel spatial and contrast interactive super-resolution network (SCINet) for assisting infrared UAV target detection. The network consists of two main subnetworks: a spatial enhancement branch (SEB) and a contrast enhancement branch (CEB). The SEB embeds the lightweight convolution module and attention mechanism to highlight the spatial structure detail features of infrared UAV targets. The proposed CEB incorporates the center-oriented contrast-aware module and multibranch collapsible module, which can provide local contrast priors to reconstruct the super-resolved UAV target image. The spatial features of the intermediate layers from the SEB are integrated into the CEB as a rich gradient prior. Besides, the output features of the CEB are aggregated into those of the SEB for further supplementing the contrast of spatial features in return. Dual-branch feature interaction (DBFI) of the SEB and CEB can further enhance the spatial details and target saliency of the targets. In addition, we also introduce an infrared UAV detection network via a new dual-dimensional feature calibration module (DFCM) for boosting the detection performance. Extensive experiments demonstrate that the SCINet outperforms the state-of-the-art (SOTA) SR methods on real infrared UAV sequences and improves the detection performance of infrared small UAV targets. The code is available athttps://github.com/IVPLaboratory/SCINet.

Cloud computing is critical for today's information society. In this paper, we shed light on two radically different cloud design philosophies: theDC-cloudbuilt around massive data centers, and theISP-cloudbuilt upon a large ISP. With extensive measurements on Alibaba, Tencent, and CTYun, we find that both designs have strengths and weaknesses: the ISP-cloud of CTYun has less inflated paths to users within the same ISP, but its paths to external users are more inflated comparing with the DC-clouds of Alibaba and Tencent. By analyzing the clouds’ routing policies, we reveal the reasons behind the path inflations: Alibaba and Tencent adopt anearly-exitpolicy to use more inflated public Internet paths as early as possible; while CTYun follows aglobal and location-agnosticpolicy to detour traffic to remote PoPs, leading to highly inflated paths. Based on the insights, we suggest alternative policies and averagely reduce 11.0% latency to 30.5% destinations for Alibaba, 9.8% latency to 18.6% destinations for Tencent, and 54.1% latency to external destinations for CTYun. The results suggest that both cloud designs have rooms for improvement, and an ISP-cloud has the potential to achieve a superior performance, thanks to its inherited advantages from the ISP infrastructure.

Memristive LSTM networks have been proven as a powerful Neuromorphic Computing Architecture (NCA) for various time series forecasting tasks and are recognized as the next generation of AI. However, a lack of model explainability makes it hard to properly interpret forecasting results for existing memristive LSTM networks, which makes this NCA unreliable, unaccountable and untrustworthy. In this paper, an interpretable memristive (IM) LSTM network design is proposed for time series forecasting, where the mixture attention technique is embedded into IM-LSTM cells for characterizing the variable-wise feature and the temporal importance. The updating rules and training approach are also presented for this interpretable memristive LSTM network. We evaluate this approach on a probabilistic residential load forecasting task incorporating PV. By improving model interpretability, the most influential predictive factors can be verified by Built Environment domain experts, demonstrating the effectiveness of our design.

The filtered backprojection (FBP) algorithm is a popular choice for complicated trajectory synthetic aperture radar (SAR) image formation processing due to its inherent nonlinear motion compensation capability. However, how to efficiently refocus the defocused FBP imagery when the motion measurement is not accurate enough is still a challenging problem. In this paper, a new interpretation of the FBP derivation is presented from the Fourier transform point of view. Based on this new viewpoint, the property of the residual 2-D phase error in FBP imagery is analyzed in detail. Then, by incorporating the derived a priori knowledge on the 2-D phase error, an accurate and efficient 2-D autofocus approach is proposed. This new approach performs the parameter estimation in a dimension-reduced parameter subspace by exploiting the a priori analytical structure of the 2-D phase error, therefore it possesses much higher accuracy and efficiency than the conventional blind methods. Finally, experimental results clearly demonstrate the effectiveness and robustness of the proposed method.

Mixed resolution format plays an important role in 3DTV coding scheme. In recent years, several research works discussed the Super Resolution (SR) problem in Mixed resolution format, where the SR image is created using the up-sampled low resolution (LR) image and the high frequency components borrowed from the adjacent high resolution (HR) warping image. However, the quality of the obtained HR images is impaired by the misalignment error caused by depth error in 3D warping. A novel SR reconstruction model is proposed in this paper. It is composed of three components: structure term, detail information term and the regularization term. They were used to preserve the structure similarity of the LR image, to preserve the detail information from the adjacent HR image, and to ensure uniqueness of the solution respectively. Simulation results show that the proposed method achieves high performance in PSNR.