In wireless communications, collaborative spectrum sensing is a process that leverages radio frequency (RF) data from multiple RF sensors to make more informed decisions and lower the overall risk of failure in distri...
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This research presents an overview of the Finite impulse response (FIR) filter architectures that utilize distributed arithmetic design (DAD). This paper reviews non-reconfigurable as well as reconfigurable FIR filter...
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With the development of 5G technology, mobile edge computing (MEC) is becoming a useful architecture, which is envisioned as a cloud extension version. Users within MEC system could save plenty of time on data transmi...
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There has been a widespread study on multi-sensor integration to achieve precise and robust odometry for autonomous vehicles (AVs) in urban areas. LiDAR odometry and visual odometry can be affected by structureless sc...
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ISBN:
(纸本)9798350399462
There has been a widespread study on multi-sensor integration to achieve precise and robust odometry for autonomous vehicles (AVs) in urban areas. LiDAR odometry and visual odometry can be affected by structureless scenarios and numerous dynamic objects. GNSS positioning can be degenerated due to the multipath and non-line-of-sight signals by buildings. Therefore, selecting appropriate weighing for heterogeneous sensors is a challenge for multi-sensor fusion. With the advancements in cellular vehicle-to-everything (C-V2X) and intelligent roadside units (RSUs), vehicles and the RSUs can collaborate to deliver reliable service. Inspired by this, this paper investigates continuous error maps for available sensors under different time conditions (noon, sunset, and night) to improve the positioning performance of surrounding AVs in complex urban environments. In particular, this paper presents an error-map-aided multi-sensor integrated system, which benefits from the error information collected by a sensor-rich AV. Then the error information is uploaded to the RSUs which is then distributed to the AVs. A smaller weight is assigned if a larger error is queried from the error map. To validate our approach, experiments were performed using the realistic CARLA simulator and our self-developed GNSS RUMS simulator. To benefit the research community, we open-sourced the implementation on our project page(3).
The optical waveguide bending sensor offers numerous advantages, including high sensitivity, compact size, immunity to electromagnetic interference, corrosion resistance, and biocompatibility. These attributes have le...
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With the rapid increase of DER(distributed energy resources), it has become a global trend to establish a distributed energy trading markets to coordinate these DERs. However, the construction of distributed energy tr...
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ISBN:
(数字)9781665488792
ISBN:
(纸本)9781665488792
With the rapid increase of DER(distributed energy resources), it has become a global trend to establish a distributed energy trading markets to coordinate these DERs. However, the construction of distributed energy trading market faces various challenges, in which trusted transaction among market participants is crisis. Since the existing solutions need numerical calculation and communication resource that lead to large delay, this paper proposes a distributed energy trading framework with secure and effective consensus protocol. Different from the traditional PBFT (Practical Byzantine Fault Tolerance) that is the notable debasement on interaction flow between processes, a threshold signature mechanism is introduced and the consensus process of trading is reconstructed. Theoretical analysis shows that the communication complexity of the proposed framework is optimized from O(n(2)) to O(n), and the computation complexity of the participants is also reduced from O(n) to O(1). Our simulation results also show that the task delay of our consensus protocol begins to be lower than PBFT when the number of participants exceeds a certain threshold. In particular, the trusted transaction delay using our consensus protocol is reduced by 12.14% when the number of participants increases to 100.
The abstract introduces a smart parking system designed to alleviate traffic congestion and save time by employing sensors, data protocols, and a live interface through mobile apps. The initial section of this paper p...
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Propelled by the growing availability of broadband connection in recent years, the gaming industry is now devoting a considerable amount of resources and investments in online and cloud gaming. In legacy online gaming...
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ISBN:
(数字)9781665488792
ISBN:
(纸本)9781665488792
Propelled by the growing availability of broadband connection in recent years, the gaming industry is now devoting a considerable amount of resources and investments in online and cloud gaming. In legacy online gaming, the gaming experience is usually provided with the support of remote servers, and online players rely on their local PCs or consoles, which hold a local copy of all the content (assets) and must follow some minimum requirements in hardware and software specifications. Whereas, on cloud gaming, intensive computational tasks are almost completely offloaded to dedicated servers: video frames are rendered on the remote machine, encoded and sent to the players as a video stream. This approach soften the need for updated and powerful devices, but it suffers from all the limitations and problems inherent to multimedia real-time streaming. In this paper we explore an hybrid approach between a (video) streaming-based cloud gaming and the traditional approach where all assets are local to the player. We propose a solution where the rendering pipeline is split between server and client. In this distributed architecture, the server manages most of the game scene description, runs the game simulation, performs the first segment of the graphics pipeline's application stage, and finally sends a stream of pre-processed graphical objects to the client, which performs the final rendering steps. The proposed approach reduces the computational burden on the server, which is not required to perform rendering, improving scalabihty when compared with cloud gaming solutions based on video streaming.
A novel, high-density, miniature and compact chip- less RFID sensor tag is presented in this article. The proposed work comprises fourteen nested quatrefoil-shaped slot resonators for ID coding with the additional fea...
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Graph processing mainly includes two stages, namely, preprocessing and algorithm execution. Most previous proposals for performance enhancement of graph processing systems focus on the algorithm execution stage, and s...
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ISBN:
(数字)9781665471770
ISBN:
(纸本)9781665471770
Graph processing mainly includes two stages, namely, preprocessing and algorithm execution. Most previous proposals for performance enhancement of graph processing systems focus on the algorithm execution stage, and simple ignore the preprocessing overhead. However, in this work, we argue that the cost of preprocessing can not be ignored since the preprocessing time is much longer than the algorithm execution time in state-of-the-art systems. We propose EndGraph, a distributed graph preprocessing system, to improve preprocessing performance. Firstly, for graph partitioning, we find existing systems either assign imbalanced preprocessing workloads or spend too much time on graph partitioning. Hence, EndGraph proposes a novel chunk-based partition algorithm to balance preprocessing workloads and achieve theoretical lower bound of time complexity. Secondly, for graph construction (converting data layout from edge array to adjacency list), existing systems use counting sort, which is not efficient for computation and communication. EndGraph employs a novel two-level graph construction method by carefully decoupling the graph construction into intra-machine and intermachine construction. Our extensive evaluation results show that, compared with five state-of-the-art systems, LFGraph, PowerLyra, PowerGraph, D-Galois, and Gemini, EndGraph can improve the preprocessing performance up to 35.76x (from 4.72x). To show the generality of EndGraph, we integrate it with D-Galois and Gemini, and it improves the end-to-end (including preprocessing and algorithm execution) graph processing performance up to 7.44 x (from 2.96 x ).
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