This study presents a mobile edge computing (MEC)-enabled UAV communication system, where a number of UAVs are served by terrestrial base stations (TBSs) equipped with computation resource in the non-orthogonal multip...
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This study presents a mobile edge computing (MEC)-enabled UAV communication system, where a number of UAVs are served by terrestrial base stations (TBSs) equipped with computation resource in the non-orthogonal multiple access manner. Each UAV has to offload its computing tasks to the proper TBS due to the limited energy supply. For this, the authors aim at minimising the sum of transmission energy of UAVs and computation energy of TBSs through jointly optimising the UAV transmit power, computation resource allocation, and UAV grouping. Considering the non-convexity of this optimisation problem, they obtain the optimal solution in the coupled steps: the convex resource allocation optimisation and the combinatorial UAV grouping optimisation. By exploiting the convex nature of the resource allocation optimisation problem, they obtain the optimal transmit power and computation allocation based on the KKT conditions and the idea of gradient descent method when considering a single TBS. Then, they adopt the simulated annealing to obtain the optimal UAV grouping and TBS selection based on the proposed resource allocation optimisation algorithm. Finally, simulation results show that the proposed joint optimisation of transmit power, computation resource allocation, and UAV grouping can effectively reduce the energy consumption of MEC-aware UAV communication system.
Modern recommender systems are built upon computation-intensive infrastructure, and it is challenging to perform real-time computation for each request, especially in peak periods, due to the limited computational res...
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ISBN:
(纸本)9798400705052
Modern recommender systems are built upon computation-intensive infrastructure, and it is challenging to perform real-time computation for each request, especially in peak periods, due to the limited computational resources. Recommending by user-wise result caches is widely used when the system cannot afford a real-time recommendation. However, it is challenging to allocate real-time and cached recommendations to maximize the users' overall engagement. This paper shows two key challenges to cache allocation, i.e., the value-strategy dependency and the streaming allocation. Then, we propose a reinforcement prediction-allocation framework (RPAF) to address these issues. RPAF is a reinforcement-learning-based two-stage framework containing prediction and allocation stages. The prediction stage estimates the values of the cache choices considering the value-strategy dependency, and the allocation stage determines the cache choices for each individual request while satisfying the global budget constraint. We show that the challenge of training RPAF includes globality and the strictness of budget constraints, and a relaxed local allocator (RLA) is proposed to address this issue. Moreover, a PoolRank algorithm is used in the allocation stage to deal with the streaming allocation problem. Experiments show that RPAF significantly improves users' engagement under computational budget constraints.
In this study, a computation-aware scheme for motion estimation (ME) in MEG-4 is proposed. The objective of the proposed scheme is to properly distribute the available AE computations of the MPEG-4 encoder. Here, the ...
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ISBN:
(纸本)9780780397538
In this study, a computation-aware scheme for motion estimation (ME) in MEG-4 is proposed. The objective of the proposed scheme is to properly distribute the available AE computations of the MPEG-4 encoder. Here, the temporal motion vector prediction technique is used to get the predicted motion vector (PMV) of each macroblock (MB). The sum of absolute components of the PMVs of all the AMs in a videofirame is used as the measure to allocate the target computation to a video frame. The proposed scheme contains three phases:1).frame-level computation allocation, 2) AM-level computation allocation, and 3) mode-level computation allocation. As compared with the comparison scheme, the proposed computation-aware scheme for motion estimation in MPEG-4 usually hay the better *** (average PSNR and bit rate degradations) in most simulation cases.
For the real-time video encoding on mobile devices,a Qo E calculation model with two experience dimensions is designed in this paper,so users can select and adjust between video quality and *** Qo E calculation model ...
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ISBN:
(纸本)9781479948109
For the real-time video encoding on mobile devices,a Qo E calculation model with two experience dimensions is designed in this paper,so users can select and adjust between video quality and *** Qo E calculation model is used in the proposed novel one-pass computation-aware motion estimation algorithm in which blocks are divided into four *** computation which a frame can use is divided into two parts of the base layer and the gain layer,and then is allocated to four types of blocks respectively,meanwhile early termination detection and adaptive computation allocation strategy are taken in the *** results show that our algorithm can more accurately to allocate computation and the Qo E calculation model has practicality.
Multi-arm harvesting robots offer a promising solution to the labor shortage in fruit harvesting, due to their ability to improve harvesting efficiency. However, multi-arm harvesters necessitate additional visual sens...
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Multi-arm harvesting robots offer a promising solution to the labor shortage in fruit harvesting, due to their ability to improve harvesting efficiency. However, multi-arm harvesters necessitate additional visual sensors to acquire distribution information of fruits within larger working spaces. Greater demands are consequently imposed on graphics computation, leading to increased costs in computing hardware of robot system. To balance the graphics computing cost and reduce energy consumption, distributed graphics computation frameworks for multi-arm robot vision system are proposed in this study. First, a host-edge framework is proposed to assign the tasks of image inference and depth alignment to host computer and edge computing modules through a decentralized mode of local connection. Moreover, to increase the endurance time of robot in application, the edge computing modules are reduced and the fifth generation mobile communication is integrated into robot graphics computing system to transfer on-board image processing to a remote computing server with MQTT protocol. To verify the effectiveness of the proposed framework, comprehensive experiments were performed, demonstrating that, compared with traditional computing framework, the proposed local distributed framework reduced 35.6% average time consumption, and over 20 FPS average processing speed can be achieve. The remote distributed framework has reduced the computational power consumption of the on-board system by approximately 23.1% while ensuring the performance is not lower than the local distributed framework. Finally, by discussing the two frameworks in terms of stability and cost, we present the commercial viability for the application of multi-arm harvesting robot.
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