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| 1 | Energy-Optimal and Delay-Bounded Computation Offloading in Mobile Edge Computing with Heterogeneous Clouds显示文摘By Mobile Edge Computing(MEC), computation-intensive tasks are offloaded from mobile devices to cloud servers, and thus the energy consumption of mobile devices can be notably reduced. In this paper, we study task offloading in multi-user MEC systems with heterogeneous clouds, including edge clouds and remote clouds. Tasks are forwarded from mobile devices to edge clouds via wireless channels, and they can be further forwarded to remote clouds via the Internet. Our objective is to minimize the total energy consumption of multiple mobile devices, subject to bounded-delay requirements of tasks. Based on dynamic programming, we propose an algorithm that minimizes the energy consumption, by jointly allocating bandwidth and computational resources to mobile devices. The algorithm is of pseudo-polynomial complexity. To further reduce the complexity, we propose an approximation algorithm with energy discretization, and its total energy consumption is proved to be within a bounded gap from the optimum. Simulation results show that, nearly 82.7% energy of mobile devices can be saved by task offloading compared with mobile device execution. | Tianchu Zhao Sheng Zhou Linqi Song Zhiyuan Jiang Xueying Guo Zhisheng Niu | 2020 | China Communications2020，17，5： | 18 |
| 2 | Wireless recommendations for internet of vehicles:Recent advances,challenges,and opportunities显示文摘Internet of Vehicles(IoV)is a distributed network of connected cars,roadside infrastructure,wireless communication networks,and central cloud platforms.Wireless recommendations play an important role in the IoV network,for example,recommending appropriate routes,recommending driving strategies,and recommending content.In this paper,we review some of the key techniques in recommendations and discuss what are the opportunities and challenges to deploy these wireless recommendations in the IoV. | Tan Li Congduan Li Jingjing Luo Linqi Song | 2020 | Intelligent and Converged Networks2020，1，1： | 2 |
| 3 | Persistence of VRC01-resistant HIV-1 during antiretroviral therapy显示文摘VRC01,a broadly neutralizing monoclonal antibody(bnmAb),can neutralize a diverse array of HIV-1 isolates by mimicking CD4 binding to the envelope glycoprotein gp120.We have previously demonstrated the presence of VRC01-resistant strains in an HIV-1 infected patient during antiretroviral therapy.Here,we report follow-up studies of two subsequent samples from the same patient.With genetic and phenotypic analysis of over 70 full-length molecular clones of the HIV-1 envelope,we show that VRC01-resistant HIV-1 continued to exist and change in its proportion of the infecting virus during treatment with a highly active antiretroviral therapy.Consistent with our previous observation,the resistant phenotype was associated with a single asparagine residue at position 460(N460),a potential N-linked glycosylation site in the V5 region.The persistence and continuing evolution of VRC01-resistant HIV-1 in vivo presents a great challenge to our future preventative and therapeutic interventions based on VRC01. | GUO DongXing SHI XuanLing SONG DingKa ZHANG LinQi | 2014 | Science China(Life Sciences)2014，57，1： | 1 |
| 4 | Emma:An accurate,efficient,and multi-modality strategy for autonomous vehicle angle prediction显示文摘Autonomous driving and self-driving vehicles have become the most popular selection for customers for their convenience.Vehicle angle prediction is one of the most prevalent topics in the autonomous driving industry,that is,realizing real-time vehicle angle prediction.However,existing methods of vehicle angle prediction utilize only single-modal data to achieve model prediction,such as images captured by the camera,which limits the performance and efficiency of the prediction system.In this paper,we present Emma,a novel vehicle angle prediction strategy that achieves multi-modal prediction and is more efficient.Specifically,Emma exploits both images and inertial measurement unit(IMU)signals with a fusion network for multi-modal data fusion and vehicle angle prediction.Moreover,we design and implement a few-shot learning module in Emma for fast domain adaptation to varied scenarios(e.g.,different vehicle models).Evaluation results demonstrate that Emma achieves overall 97.5%accuracy in predicting three vehicle angle parameters(yaw,pitch,and roll),which outperforms traditional single-modalities by approximately 16.7%-36.8%.Additionally,the few-shot learning module presents promising adaptive ability and shows overall 79.8%and 88.3%accuracy in 5-shot and 10-shot settings,respectively.Finally,empirical results show that Emma reduces energy consumption by 39.7%when running on the Arduino UNO board. | Keqi Song Tao Ni Linqi Song Weitao Xu | 2023 | Intelligent and Converged Networks2023，4，1： | 0 |
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