Due to the Internet of Everything (IoE), data generated in our life become larger. As a result, we need more effort to analyze the data and extract valuable information. In the cloud computing environment, all data analysis is done in the cloud, and the client only needs less computing power to handle some simple tasks. However, with the rapid increase in data volume, sending all data to the cloud via the Internet has become more expensive. The required cloud computing resources have also become larger. To solve this problem, edge computing is proposed. Edge is granted with more computation power to process data before sending it to the cloud. Therefore, the data transmitted over the Internet and the computing resources required by the cloud can be effectively reduced. In this work, we proposed an edge-assisted parallel uncertain skyline (EPUS) algorithm for emerging low-latency IoE analytic applications. We use the concept of skyline candidate set to prune data that are less likely to become the skyline data on the parallel edge computing nodes (ECNs). With the candidate skyline set, each ECN only sends the information required to the server for updating the global skyline, which reduces the amount of data that transfer over the Internet. According to the simulation results, the proposed method is better than two comparative methods, which reduces the latency of processing 2-D data by more than 50%. For high-dimensional data, the proposed EPUS method also outperforms the other existing methods.

The wavelength-routed optical network-on-chip (WRONoC) emerges as a promising solution for multi-core system communication, providing high-bandwidth, high-speed, and low-power transmission. As the number of cores in a WRONoC increases, however, some WRONoC topologies could be infeasible with bandwidth and crosstalk constraints if bit-level parallelism is not considered during topology generation. Previous work optimized the parallelism only for the radius selection of microring resonators but not for topology generation. To remedy this drawback, we present a parallelism-aware WRONoC topology generation flow. The proposed flow guarantees to generate a parallelism-optimal topology for full connectivity; and a parallelism-optimal topology for customized connectivity if the netlist meets certain conditions. Compared with the state-of-the-art methods, experimental results show a 67.5% improvement in parallelism.

The wavelength-routed optical network-on-chip (WRONoC) is a promising solution for advanced signal communication because of its high-bandwidth, low-latency, and power-efficient signal transmissions. Existing WRONoC topology designs rely on pre-defined network templates with limited solution space and design purposes. To remedy these disadvantages, we present a general model for WRONoC topologies. Based on this model, we propose a novel design flow for customized and fault-tolerant topologies to minimize the maximum insertion loss and the wavelength and micro-ring resonator (MRR) usage. Besides, we present two fault-tolerant topologies for full-connectivity netlists, namely the Actin-STAR and Zygo-STAR topologies. We prove that the Actin-STAR topology has a performance bound of 2.22 in the primary-path maximum insertion loss, and the Zygo-STAR topology has a performance bound of 1.39 in the backup-path one. Experimental results show that our designs significantly outperform the state-of-the-art designs in wavelength, MRR usage, and maximum insertion loss.

The wavelength-routed optical network on chip (WRONoC) is a promising solution for signal transmission in modern System-on-Chip (SoC) designs. Previous works do not simultaneously handle all the following four main issues for WRONoCs: 1) correlations between the topological structure and physical layout; 2) tradeoffs between the maximum insertion loss and the number of wavelengths; 3) runtime scalability of wavelength assignment scheme; and 4) a fully automated flow to generate predictable designs. As a result, their insertion loss estimation is inaccurate, their wavelength assignment is inefficient, and thus, only suboptimal results are obtained. To remedy these disadvantages, we present a fully automated topological structure and a physical layout co-design flow with improved wavelength assignment schemes to minimize the maximum insertion loss and the laser power simultaneously with a significant speedup. The experimental results show that our co-design flow significantly outperforms state-of-the-art works in the maximum insertion loss, laser power, and runtimes.

With the advancement of technology, the data generated in our lives is getting faster and faster, and the amount of data that various applications need to process becomes extremely huge. Therefore, we need to put more effort into analyzing data and extracting valuable information. Cloud computing used to be a good technology to solve a large number of data analysis problems. However, in the era of the popularity of the Internet of Things (IoT), transmitting sensing data back to the cloud for centralized data analysis will consume a lot of wireless communication and network transmission costs. To solve the above problems, edge computing has become a promising solution. In this paper, we propose a new algorithm for processing probabilistic skyline queries over uncertain data streams in an edge computing environment. We use the concept of a second skyline set to filter data that is unlikely to be the result of the skyline. Besides, the edge server only sends the information needed to update the global analysis results on the cloud server, which will greatly reduce the amount of data transmitted over the network. The results show that our proposed method not only reduces the response time by more than 50% compared with the brute force method on two-dimensional data but also maintains the leading processing speed on high-dimensional data.

The vector graphics with gradient mesh can be attributed to their compactness and scalability; however, they tend to fall short when it comes to real-time editing due to a lack of real-time rasterization and an efficient editing tool for image details. In this paper, we encode global manipulation geometries and local image details within a hybrid vector structure, using parametric patches and detailed features for localized and parallelized thin-plate spline interpolation in order to achieve good compressibility, interactive expressibility, and editability. The proposed system then automatically extracts an optimal set of detailed color features while considering the compression ratio of the image as well as reconstruction error and its characteristics applicable to the preservation of structural and irregular saliency of the image. The proposed real-time vector representation makes it possible to construct an interactive editing system for detail-maintained image magnification and color editing as well as material replacement in cross mapping, without maintaining spatial and temporal consistency while editing in a raster space. Experiments demonstrate that our representation method is superior to several state-of-the-art methods and as good as JPEG, while providing real-time editability and preserving structural and irregular saliency information.