The recent proliferation of Fifth-Generation(5G)networks and Sixth-Generation(6G)networks has given rise to Vehicular Crowd Sensing(VCS)systems which solve parking collisions by effectively incentivizing vehicle ***,i...
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The recent proliferation of Fifth-Generation(5G)networks and Sixth-Generation(6G)networks has given rise to Vehicular Crowd Sensing(VCS)systems which solve parking collisions by effectively incentivizing vehicle ***,instead of being an isolated module,the incentive mechanism usually interacts with other *** on this,we capture this synergy and propose a Collision-free Parking Recommendation(CPR),a novel VCS system framework that integrates an incentive mechanism,a non-cooperative VCS game,and a multi-agent reinforcement learning algorithm,to derive an optimal parking strategy in real ***,we utilize an LSTM method to predict parking areas roughly for recommendations *** incentive mechanism is designed to motivate vehicle participation by considering dynamically priced parking tasks and social network *** order to cope with stochastic parking collisions,its non-cooperative VCS game further analyzes the uncertain interactions between vehicles in parking *** its multi-agent reinforcement learning algorithm models the VCS campaign as a multi-agent Markov decision process that not only derives the optimal collision-free parking strategy for each vehicle independently,but also proves that the optimal parking strategy for each vehicle is ***,numerical results demonstrate that CPR can accomplish parking tasks at a 99.7%accuracy compared with other baselines,efficiently recommending parking spaces.
The rapid advancement of artificial intelligence applications has resulted in the deployment of a growing number of deep neural networks (DNNs) on mobile devices. Given the limited computational capabilities and small...
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The widespread adoption of wearable devices has led to a surge in the development of multi-device wearable human activity recognition (WHAR) systems. Nevertheless, the performance of traditional supervised learning-ba...
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The Processing-In-Memory (PIM) architecture becomes a promising candidate for deep learning accelerators by integrating computation and memory. Most PIM-based studies improve the performance and energy efficiency by u...
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The Processing-In-Memory (PIM) architecture becomes a promising candidate for deep learning accelerators by integrating computation and memory. Most PIM-based studies improve the performance and energy efficiency by using the Weight Stationary (WS) data flow due to its high parallelism. However, the WS data flow has some fundamental limitations. First, the WS data flow has huge activation movements between on-chip memory and off-chip memory due to the limited memory space of the ReRAM array. Second, the WS data flow needs to read the input activation repeatedly according to the convolution window. These data movements decrease the energy efficiency and performance of the PIM architecture. To address these issues, the IS data flow stores activations instead of weights to reduce data movements. But the IS data flow faces some challenges. First, the data dependency between adjacent layers limits the performance. Second, there are huge across-array computations due to the special mapping method. Third, the previous IS data flow cannot realize the high parallelism. Fourth, the IS data flow depends on the three-dimensional (3D) ReRAM structure. To address these issues, we propose a novel data flow for PIM architectures. We optimize the IS data flow to decrease the activation movement and propose a parallel computing method to realize high parallelism and reduce the across-array computations. We identify and analyze the fundamental limitations and impact of different inter-layer data flows, including the WS-WS, IS-IS, WS-IS, and IS-WS. We also propose a method to build a hybrid data flow by combining these inter-layer data flows to trade-off performance and energy consumption. Our experimental results and analysis demonstrate the potential of our design. The performance and energy efficiency of our design reaches 0.13 TFLOPS∼1.77 TFLOPS and 61 TOPS/J∼85 TOPS/J, respectively. Compared to the state-of-the-art design, the NEBULA, our design can improve performance by 1.4×, 2.
Network-on-Chip(NoC)is widely adopted in neuromorphic processors to support communication between neurons in spiking neural networks(SNNs).However,SNNs generate enormous spiking packets due to the one-to-many traffic ...
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Network-on-Chip(NoC)is widely adopted in neuromorphic processors to support communication between neurons in spiking neural networks(SNNs).However,SNNs generate enormous spiking packets due to the one-to-many traffic *** spiking packets may cause communication pressure on *** propose a path-based multicast routing method to alleviate the ***,all destination nodes of each source node on NoC are divided into several ***,multicast paths in the clusters are created based on the Hamiltonian path *** proposed routing can reduce the length of path and balance the communication load of each ***,we design a lightweight microarchitecture of NoC,which involves a customized multicast packet and a routing *** use six datasets to verify the proposed multicast *** with unicast routing,the running time of path-based multicast routing achieves 5.1x speedup,and the number of hops and the maximum transmission latency of path-based multicast routing are reduced by 68.9%and 77.4%,*** maximum length of path is reduced by 68.3%and 67.2%compared with the dual-path(DP)and multi-path(MP)multicast routing,***,the proposed multicast routing has improved performance in terms of average latency and throughput compared with the DP or MP multicast routing.
Semantic segmentation of driving scene images is crucial for autonomous *** deep learning technology has significantly improved daytime image semantic segmentation,nighttime images pose challenges due to factors like ...
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Semantic segmentation of driving scene images is crucial for autonomous *** deep learning technology has significantly improved daytime image semantic segmentation,nighttime images pose challenges due to factors like poor lighting and overexposure,making it difficult to recognize small *** address this,we propose an Image Adaptive Enhancement(IAEN)module comprising a parameter predictor(Edip),multiple image processing filters(Mdif),and a Detail Processing Module(DPM).Edip combines image processing filters to predict parameters like exposure and hue,optimizing image *** adopt a novel image encoder to enhance parameter prediction accuracy by enabling Edip to handle features at different *** strengthens overlooked image details,extending the IAEN module’s *** the segmentation network,we integrate a Depth Guided Filter(DGF)to refine segmentation *** entire network is trained end-to-end,with segmentation results guiding parameter prediction optimization,promoting self-learning and network *** lightweight and efficient network architecture is particularly suitable for addressing challenges in nighttime image *** experiments validate significant performance improvements of our approach on the ACDC-night and Nightcity datasets.
Although having achieved real-time performance on mesh construction, most of the existing LiDAR odometry and meshing methods have difficulties in dealing with cluttered scenes due to relying on explicit meshing techni...
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Although having achieved real-time performance on mesh construction, most of the existing LiDAR odometry and meshing methods have difficulties in dealing with cluttered scenes due to relying on explicit meshing techniques. To overcome these limitations, we propose a real-time mesh-based LiDAR odometry and mapping approach for large-scale scenes through implicit reconstruction and a parallel spatial-hashing scheme. In order to efficiently reconstruct the triangular meshes using the implicit function, we suggest an incremental voxel meshing strategy that depends on a novel voxel map fusing scans through a single traversal of the current frame. Moreover, we introduce a scalable partition module to compress space. By taking advantage of the rapid access to triangular meshes, we design a robust odometry method with location and feature-based data association to estimate the poses between the input point clouds and the recovered triangular meshes. The experimental results on four public datasets demonstrate the effectiveness of our proposed approach in recovering both accurate motion trajectories and environmental mesh maps. IEEE
This research focuses on Scene Text Recognition (STR), a crucial component in various applications of artificial intelligence such as image retrieval, office automation, and intelligent traffic systems. Recent studies...
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Digital media interaction design mainly focuses on the user’s interactive experience in the digital media environment. By designing interaction methods that conform to human cognition and behavioral habits, it improv...
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Although deep convolution neural network(DCNN)has achieved great success in computer vision field,such models are considered to lack interpretability in *** of fundamental issues is that its decision mechanism is cons...
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Although deep convolution neural network(DCNN)has achieved great success in computer vision field,such models are considered to lack interpretability in *** of fundamental issues is that its decision mechanism is considered to be a“black-box”*** authors design the binary tree structure convolution(BTSC)module and control the activation level of particular neurons to build the interpretable DCNN ***,the authors design a BTSC module,in which each parent node generates two independent child layers,and then integrate them into a normal DCNN *** main advantages of the BTSC are as follows:1)child nodes of the different parent nodes do not interfere with each other;2)parent and child nodes can inherit ***,considering the activation level of neurons,the authors design an information coding objective to guide neural nodes to learn the particular information coding that is *** the experiments,the authors can verify that:1)the decision-making made by both the ResNet and DenseNet models can be explained well based on the"decision information flow path"(known as the decision-path)formed in the BTSC module;2)the decision-path can reasonably interpret the decision reversal mechanism(Robustness mechanism)of the DCNN model;3)the credibility of decision-making can be measured by the matching degree between the actual and expected decision-path.
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