In the mobile edge computing environment, caching data in edge storage systems can significantly reduce data retrieval latency for users while saving the costs incurred by cloud-edge data transmissions for app vendors. Existingedge data caching(EDC) methods prioritize popular data and aim to minimize users’ data retrieval latency and system storage costs jointly. However, these EDC methods often rely on the assumption that data popularity always follows certain distributions. As a result, they cannot properly adapt to the fluctuations in data popularity due to user mobility or unexpected increases in user demands. Meanwhile, unlike cloud data centers, complex and fragile edge servers are more likely to experience physical failures or network outages, presenting new challenges for EDC strategies. Specifically, when an edge server fails or experiences an outage, cached data may become temporarily unavailable, leading to increased latency as requests are redirected to alternative servers or the cloud. In this paper, to enableuncertainty-aware edge data caching(uEDC), we first model the problem as a robust optimization problem and propose an optimal algorithm named uEDC-B to find the optimal uEDC solution. To address the high computational complexity of uEDC-B, we introduce an approximate algorithm named uEDC-L based on linear decision rules. Theoretical analysis and extensive experiments on a real-world dataset demonstrate that the proposed methods outperform two state-of-the-art approaches in handling the uncertainties in data popularity and edge server failure with a significant performance improvement of 59.27% in data retrieval latency and 55.07% in data caching cost.

The emergence ofmobile edge computing(MEC) in distributed systems has sparked increased attention toward edge data management. A conflict arises from the disparity between limited edge resources and the continuously expanding data requests for data storage, making the reduction of data storage costs a critical objective. Despite the extensive studies of edge data deduplication as a data reduction technique, existing deduplication methods encounter numerous challenges in MEC environments. These challenges stem from disparities between edge servers and cloud data center edge servers, as well as uncertainties such as user mobility, leading to insufficient robustness in deduplication decision-making. Consequently, this paper presents a robust optimization-based approach for the edge data deduplication problem. By accounting for uncertainties including the number of data requirements and edge server failures, we propose two distinct solving algorithms: uEDDE-C, a two-stage algorithm based on column-and-constraint generation, and uEDDE-A, an approximation algorithm to address the high computation overhead of uEDDE-C. Our method facilitates efficient data deduplication in volatile edge network environments and maintains robustness across various uncertain scenarios. We validate the performance and robustness of uEDDE-C and uEDDE-A through theoretical analysis and experimental evaluations. The extensive experimental results demonstrate that our approach significantly reduces data storage cost and data retrieval latency while ensuring reliability in real-world MEC environments.

Video surveillance is an important data source of urban computing and intelligence. The low resolution of many existing video surveillance devices affects the efficiency of urban computing and intelligence. Therefore, improving the resolution of video surveillance is one of the important tasks of urban computing and intelligence. In this paper, the resolution of video is improved by superresolution reconstruction based on a learning method. Different from the superresolution reconstruction of static images, the superresolution reconstruction of video is characterized by the application of motion information. However, there are few studies in this area so far. Aimed at fully exploring motion information to improve the superresolution of video, this paper proposes a superresolution reconstruction method based on an efficient subpixel convolutional neural network, where the optical flow is introduced in the deep learning network. Fusing the optical flow features between successive frames can compensate for information in frames and generate high-quality superresolution results. In addition, in order to improve the superresolution, a superpixel convolution layer is added after the deep convolution network. Finally, experimental evaluations demonstrate the satisfying performance of our method compared with previous methods and other deep learning networks; our method is more efficient.

Conventional image classification models commonly adopt a single feature vector to represent informative contents. However, a single image feature system can hardly extract the entirety of the information contained in images, and traditional encoding methods have a large loss of feature information. Aiming to solve this problem, this paper proposes a feature fusion-based image classification model. This model combines the principal component analysis (PCA) algorithm, processed scale invariant feature transform (P-SIFT) and color naming (CN) features to generate mutually independent image representation factors. At the encoding stage of the scale-invariant feature transform (SIFT) feature, the bag-of-visual-word model (BOVW) is used for feature reconstruction. Simultaneously, in order to introduce the spatial information to our extracted features, the rotation invariant spatial pyramid mapping method is introduced for the P-SIFT and CN feature division and representation. At the stage of feature fusion, we adopt a support vector machine with two kernels (SVM-2K) algorithm, which divides the training process into two stages and finally learns the knowledge from the corresponding kernel matrix for the classification performance improvement. The experiments show that the proposed method can effectively improve the accuracy of image description and the precision of image classification.

Summary There are two problems in global over‐complete dictionary: lack of adaptability to image local structure and low computational efficiency. Based on the study of the adaptive sparse representation reconstruction, this paper obtained a series of corresponding sub‐dictionaries by image block subset, then the optimal sub‐dictionary for each reconstruction image block is adaptively selected, which can be more accurately sparse represented modeled to improve the effect and efficiency of the algorithm. In order to promote the ability of sparse representation model, nonlocal self‐similarity prior item is introduced. Meanwhile, the nonlocal self‐similarity model is improved by using the idea of the bilateral filter, and the space distance restraint between pixels is introduced to better keep the image edge information. Moreover, the nonlocal self‐similar distance measure is improved to reduce the amount of calculation. Experimental results show that the proposed algorithm can effectively suppress noise effects and can maintain the image edge details, at the same time, there are certain advantages at both the peak signal to noise ratio (PSNR) and visual effects.

Web-based catch-up TV has revolutionised watching habits as it provides users the opportunity to watch programs at their preferred time and place, using a variety of devices. With the increasing offer of TV content, there is an emergent need for personalised recommendation solutions, which help users to select programs of interest. In this work, we study the watching patterns of users of an Australian nation-wide catch-up TV service provider and develop a suite of approaches for a catch-up recommendation scenario. We evaluate these approaches using a new large-scale dataset gathered by the Web-based catch-up portal deployed by the provider. The evaluation allows us to compare the performance of several recommenders that address the discovery of both TV programs already watched by users and new programs that users may find relevant.

In order to decrease the spectral distortion using the IHS-based fusion method and more effectively inject the spatial detail information of high spatial resolution images into the fused image, this paper presents a new image fusion method that combines the IHS transform with dual-tree complex wavelet transform. The proposed method can overcome the distortion of spectrum. Firstly, intensity-hue-saturation (IHS) transform of multispectral(MS) image is implemented. Then panchromatic (PAN) image and the intensity component of MS images are decomposed into three scales using dual-tree complex wavelet transform respectively. After that, the region-based fusion operator is adopted to select the wavelet coefficients by fusing the wavelet coefficients of PAN image and the intensity component of MS at each scale in wavelet domain. Further, the fused intensity component is obtained by inverse dual-tree complex wavelet transform. Finally, the fused image is obtained by inverse IHS transform. The experiment results demonstrate our proposed method is effective.