Aggregate computing is an emerging macropro-gramming paradigm, whose validation is often performed by simulation. In this work, we compare the existing JVM-based toolkits from a performance point of view and show that...
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ISBN:
(数字)9798331527211
ISBN:
(纸本)9798331527228
Aggregate computing is an emerging macropro-gramming paradigm, whose validation is often performed by simulation. In this work, we compare the existing JVM-based toolkits from a performance point of view and show that they can be improved by adopting a different technique for implementing a core part of the semantics, called alignment, based on meta-programming implemented with a compiler plugin.
Aiming at the problem that complex radar emitter signals are difficult to be recognized at low signal-to-noise ratio, a method based on improved coordinate attention network is proposed. Firstly, the radar signal is c...
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ISBN:
(纸本)9781665454636
Aiming at the problem that complex radar emitter signals are difficult to be recognized at low signal-to-noise ratio, a method based on improved coordinate attention network is proposed. Firstly, the radar signal is converted into a two-dimensional time-frequency image to reflect the signal feature information. Then the time-frequency image preprocessing and denoising by convolutional neural network. Finally, the coordinated attention network is used for feature extraction, and then the classification of radar emitter source signals are realized. Experiments results show that the proposed method can validly improve the accuracy of radar signal recognition under the condition of low SNR.
Wildfire, especially uncontrolled flammable vegetation hazard in rural or wilderness areas, seriously affected human life and safety, prediction methods has been researched widely in recent decades. According to Amazo...
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Cloud-based services are a current approach for developing large-scale applications with advantages such as flexibility, access to on-demand resources, and business agility. The overall application functionality resul...
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With the development of the mobile Internet, supporting multi-user has become a popular concern for Internet-based algorithms. In this paper, an efficient watermark embedding framework is proposed to achieve secure ou...
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ISBN:
(纸本)9781665454636
With the development of the mobile Internet, supporting multi-user has become a popular concern for Internet-based algorithms. In this paper, an efficient watermark embedding framework is proposed to achieve secure outsourced image watermarking in multi-user settings. In addition, an authentication mechanism for authorized users is designed to ensure the security and correctness of the information. The proposed method enables watermarking operations in complex outsourced environments while embedding watermarks in encrypted images of multiple users. The experimental results show that the framework is feasible and scalable.
A major cause of traffic accidents is road rage. How to identify road rage is an important problem that needs to be solved urgently. Road rage recognition is different from traditional emotion recognition. The sound s...
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ISBN:
(纸本)9781665454636
A major cause of traffic accidents is road rage. How to identify road rage is an important problem that needs to be solved urgently. Road rage recognition is different from traditional emotion recognition. The sound signal to be recognized contains complex traffic environment noise, and the recognition target is a single anger emotion. This paper extracts high robustness, high generalization, and anger features from speech signals. A convolutional neural network (CNN) and multi-headed self-attention criterion bi-directional long-short-term memory network (Multi-headed Self-Attention Bi-LSTM) fusion decision model is proposed to realize anger emotion recognition.
In order to improve the sampling efficiency of the non-sequential Monte Carlo simulation method, an improved hybrid method combining the analytical method and the significant Latin hypercube sampling method is propose...
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ISBN:
(纸本)9781665454636
In order to improve the sampling efficiency of the non-sequential Monte Carlo simulation method, an improved hybrid method combining the analytical method and the significant Latin hypercube sampling method is proposed based on the idea of state space partitioning. The method partitions the system state space based on the determination of the significant state subspace to avoid sampling the zero-fault state of the system. The reliability index of the significant state subspace is efficiently solved by the analytical method, and the remaining state subspace is sampled by the significant Latin hypercube sampling method. Finally, the correctness and efficiency of the proposed algorithm is verified by evaluating the reliability of the IEEE-RTS system.
The house price index plays a very important role in the real estate economy due to it is an indicator of real estate price changing. Aiming at the compilation model of China's new ordinary residential housing pri...
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Driverless technology has become a hot spot of current research, in order to improve the speed of unmanned vehicle path planning. Firstly, the artificial potential field (APF) model is added to the objective function ...
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Random opportunistic networks are dynamic, resulting in nodes not being able to sense the state of the network, and the network topology of nodes changes all the time. Therefore, this paper proposes a RIS-aided channe...
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ISBN:
(纸本)9781665454636
Random opportunistic networks are dynamic, resulting in nodes not being able to sense the state of the network, and the network topology of nodes changes all the time. Therefore, this paper proposes a RIS-aided channel construction algorithm, which can be used to maintain and change the topology of random opportunistic networks. A machine learning algorithm with spatio-temporal feature fusion is first used to predict the current position of the node, and finally the RIS-aided channel construction is implemented based on the predicted position. The simulation experiments show that the algorithm can find the optimal path between the target node and the source node in the presence of errors in the target node.
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