Event camera has offered promising alternative for visual perception, especially in high speed and high dynamic range scenes. Recently, many deep learning methods have shown great success in providing model-free solutions to many event-based problems, such as optical flow estimation. However, existing deep learning methods did not address the importance of temporal information well from the perspective of architecture design and cannot effectively extract spatio-temporal features. Another line of research that utilizes Spiking Neural Network suffers from training issues for deeper architecture. To address these points, a novel input representation is proposed that captures the events temporal distribution for signal enhancement. Moreover, we introduce a spatio-temporal recurrent encoding-decoding neural network architecture for event-based optical flow estimation, which utilizes Convolutional Gated Recurrent Units to extract feature maps from a series of event images. Besides, our architecture allows some traditional frame-based core modules, such as correlation layer and iterative residual refine scheme, to be incorporated. The network is end-to-end trained with self-supervised learning on the Multi-Vehicle Stereo Event Camera dataset. We have shown that it outperforms all the existing state-of-the-art methods by a large margin.
Based on system efficient electrostatic discharge design (SEED) methodology, this paper proposes a high-order SPICE simulation methodology to predict the performance of the ESD protection circuits. The related PCB-level experiments of the selected protection circuits are fulfilled to verify this method. As a result, the consistency of the comparison results between the simulation and measurement illustrates that the method can accurately predict the performance of system-level protection circuits.
Large-scale neuromorphic dataset is costly to construct and difficult to annotate because of the unique high-speed asynchronous imaging principle of bio-inspired cameras. Lacking of large-scale annotated neuromorphic datasets has significantly hindered the applications of bio-inspired cameras in deep neural networks. Synthesizing neuromorphic data from annotated RGB images can be considered to alleviate this challenge. This paper proposes a simulator to generate simulated spiking data from images recorded by frame cameras. To minimize the deviationsbetween synthetic data and real data, the proposed simulator named SpikingSIM considers the sensing principle of spiking cameras, and generates high-quality simulated spiking data, e.g., the noises in real data are also simulated. Experimental results show that, our simulator generates more realistic spiking data than existing methods. We hence train deep neural networks with synthesized spiking data. Experiments show that, the network trained by our simulated data generalizes well on real spiking data. The source code of SpikingSIM is available at http://github.com/Evin-X/SpikingSIM.
Mobile edge computing (MEC) has been an effective paradigm to support real-time computation-intensive vehicular applications. However, due to highly dynamic vehicular topology, these existing centralized-based or distributed-based scheduling algorithms requiring high communication overhead, are not suitable for task offloading in vehicular networks. Therefore, we investigate a novel service scenario of MEC-based vehicular crowdsourcing, where each MEC server is an independent agent and responsible for making scheduling of processing traffic data sensed by crowdsourcing vehicles. On this basis, we formulate a data-driven task offloading problem by jointly optimizing offloading decision and bandwidth/computation resource allocation, and renting cost of heterogeneous servers, such as powerful vehicles, MEC servers and cloud, which is a mixed-integer programming problem and NP-hard. To reduce high time-complexity, we propose the solution in two stages. First, we design an asynchronous deep Q-learning to determine offloading decision, which achieves fast convergence by training the local DQN model at each agent in parallel and uploading for global model update asynchronously. Second, we decompose the remaining resource allocation problem into several independent subproblems and derive optimal analytic formula based on convex theory. Lastly, we build a simulation model and conduct comprehensive simulation, which demonstrates the superiority of the proposed algorithm.