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8th International Conference on Communication, Image and Signal Processing, CCISP 2023

作者： He, Rui Xiao, Yunzhe Yang, Shaowu College of Computer Science and Technology National University of Defense Technology Quantum Information & State Key Laboratory of High Performance Computing Changsha China

ISBN: (纸本)9798350305838

Place recognition is a crucial component in the realm of simultaneous localization and mapping (SLAM) and autonomous driving. It can also enhance the performance of loop closure detection and global localization. LiDAR-based place recognition usually relies on global feature descriptors to accurately express the similarity between LiDAR scans. However, it is time-consuming to extract and generate the global feature descriptors from the original LiDAR data. As a result, treating LiDAR-based point clouds place recognition as a 2D image problem using range projection is becoming an increasingly common and successful strategy. Vision Transformer (ViT) is a Transformer model based on self-attention mechanism for image classification. Through self-attention mechanism, ViT out-performs CNN at modeling the global information of images. We investigate that whether ViT can be transferred to LiDAR-based place recognition tasks. Firstly, we introduce a new convolutional stem to replace the original linear embedding layer in ViT. Secondly, we establish synergy by integrating low-level fine-grained features from new convolution layer with high-level coarse-grained features from ViT by skip connections. Thirdly, we aggregate these features to construct the global feature descriptor via the NetVLAD technique. The results of experiments on KITTI confirm that our method can operate online and achieve superior performance on par with existing approaches. © 2023 IEEE.

关键词： Convolution

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Design and Implementation of a Compiler Supporting RISC-V Custom Cryptographic and Vector Instructions 23

Design and Implementation of a Compiler Supporting RISC-V Cu...

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7th International Conference on Computer Science and Application Engineering, CSAE 2023

作者： Zou, Xiaofeng Peng, Yuanxi Zhang, Qing Institute for Quantum Information State Key Laboratory of High Performance Computing National University of Defense Technology Changsha China Second Research Department Cloud Computing Equipment Industry Innovation Center Jinan China

ISBN: (纸本)9798400700590

Post-quantum cryptographic algorithms capable of resisting quantum computing attacks have received increasing attention in recent years, especially lattice-based cryptographic algorithms have become mainstream. Since their specialization and complex computation, the cryptographic computation is inefficient on general-purpose processors. In order to improve the performance of the processor in post-quantum cryptography computing, we have extended a set of cryptographic and vector instruction sets for the CRYSTALS-Kyber algorithm, and implemented support for custom cryptographic and vector instructions based on the open-source toolchain. These efforts can realize the compilation and disassembly of the CRYSTALS-Kyber encryption/decryption algorithm programs, and optimized the performance of the compilation. This paper mainly introduces how to design an extended instruction set based on the target algorithm and enable the compiler to support these instructions. Compared to the situation before extending custom instructions, the total number of instructions for key generation, encryption, and decryption assembly programs has decreased by 3651 times, and the total number of execution cycles has decreased by 2195 times. © 2023 ACM.

关键词： Program compilers

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Self-distillation Enhanced Vertical Wavelet Spatial Attention for Person Re-identification 1

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30th International Conference on MultiMedia Modeling, MMM 2024

作者： Zhang, Yuxuan Tan, Huibin Lan, Long Teng, Xiao Ren, Jing Zhang, Yongjun Institute for Quantum Information & State Key Laboratory of High Performance Computing College of Computer Science and Technology National University of Defense Technology Changsha410073 China Beijing100071 China

ISBN: (数字)9783031533082

ISBN: (纸本)9783031533075

Person re-identification is a challenging problem in computer vision, aiming to accurately match and recognize the same individual across different viewpoints and cameras. Due to significant variations in appearance under different scenes, person re-identification requires highly discriminative features. Wavelet features contain richer phase and amplitude information as well as rotational invariance, demonstrating good performance in various visual tasks. However, through our observations and validations, we have found that the vertical component within wavelet features exhibits stronger adaptability and discriminability in person re-identification. It better captures the body contour and detailed information of pedestrians, which is particularly helpful in distinguishing differences among individuals. Based on this observation, we propose a vertical wavelet spatial attention only with the vertical component in the high frequency specifically designed for feature extraction and matching in person re-identification. To enhance spatial semantic consistency and facilitate the transfer of knowledge between different layers of wavelet attention in the neural network, we introduce a self-distillation enhancement method to constrain shallow and deep spatial attention. Experimental results on Market-1501 and DukeMTMC-reID datasets validate the effectiveness of our model. © 2024, The Author(s), under exclusive license to Springer Nature Switzerland AG.

关键词： Distillation

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Credit assignment for trained neural networks based on Koopman operator theory

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Frontiers of Computer Science 2024年第1期18卷 251-253页

作者： Zhen LIANG Changyuan ZHAO Wanwei LIU Bai XUE Wenjing YANG Zhengbin PANG Institute for Quantum Information&State Key Laboratory of High Performance Computing National University of Defense TechnologyChangsha 410000China Institute of Software Chinese Academy of SciencesBeijing 100190China College of Computer Science and Technology National University of Defense TechnologyChangsha 410000China

1 Introduction Artificial neural networks(ANNs,also NNs)have recently emerged as leading candidate models for deep learning,popularly used in various areas[1–3].Behind the enormous success,ANNs are generally with complicated structures,there being an intricate data flow through multiple linear or nonlinear components between the input layer and the output ***,it is pressing to evaluate how much a specific component contributes to the final output,termed the Credit Assignment Problem(CAP)[4]in this paper.

关键词： layer assignment operator

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A large scale parallel fluid-structure interaction computing platform for simulating structural responses to a detonation shock

A large scale parallel fluid-structure interaction computing...

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作者： Zhang, Sen Guo, Xiao-Wei Li, Chao Liu, Yi Fan, Sijiang Zhao, Ran Yang, Canqun Institute for Quantum Information & State Key Laboratory of High Performance Computing College of Computer Science and Technology National University of Defense Technology Changsha China

Due to the intrinsic nature of multi-physics, it is prohibitively complex to design and implement a simulation software platform for study of structural responses to a detonation shock. In this article, a partitioned fluid-structure interaction computing platform is designed for parallel simulating structural responses to a detonation shock. The detonation and wave propagation are modeled in an open-source multi-component solver based on OpenFOAM and blastFoam, and the structural responses are simulated through the finite element library ***. To capture the interaction dynamics between the fluid and the structure, both solvers are adapted to preCICE. For improving the parallel performance of the computing platform, the inter-solver data is exchanged by peer-to-peer communications and the intermediate server in conventional multi-physics software is eliminated. Furthermore, the coupled solver with detonation support has been deployed on a computing cluster after considering the distributed data storage and load-balancing between solvers. The 3D numerical result of structural responses to a detonation shock is presented and analyzed. On 256 processor cores, the speedup ratio of the simulations for a detonation shock reach 178.0 with 5.1 million of mesh cells and the parallel efficiency achieve 69.5%. The results demonstrate good potential of massively parallel simulations. Overall, a general-purpose fluid-structure interaction software platform with detonation support is proposed by integrating open source codes. And this work has important practical significance for engineering application in fields of construction blasting, mining, and so forth. © 2021 John Wiley & Sons, Ltd.

关键词： Detonation

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OL-CBBA: An Online Task Allocation Algorithm under Weak Communication Conditions 29

OL-CBBA: An Online Task Allocation Algorithm under Weak Comm...

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29th IEEE International Conference on Parallel and Distributed Systems, ICPADS 2023

作者： Li, Jun Huang, Wanrong Wu, Honglin Cai, Zhongxuan Zhang, Yongjun National University of Defense Technology Institute for Quantum Information State Key Laboratory of High Performance Computing College of Computer Science and Technology Changsha China

ISBN: (纸本)9798350330717

Multiple unmanned aerial vehicles (UAVs) and multiple tasks allocation problem is difficult to solve. Existing task allocation algorithms assume that the UAVs' position is static, and cannot assign tasks with the changing UAVs' position simultaneously during task execution. Those algorithms reduce the efficiency of task allocation. In this paper, we propose an online consensus-based bundle algorithm (OL-CBBA) under weak communication for dynamic task allocation. We consider the situation that the location information of UAVs will constantly change during the dynamic execution of tasks. The algorithm first improves the static CBBA to an online algorithm by updating the task marginal score in task path. Moreover, we specify a flag for the convergence of individual tasks, allowing UAVs to start executing tasks earlier. Extensive comparative experiments prove the highly consistent efficiency of OL-CBBA under weak communication conditions. Specifically, the proposed OL-CBBA attains up to 22% improvement compared with CBBA. © 2023 IEEE.

关键词： Auction Algorithm Decentralized System Dynamic Task Allocation Weak Communication

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DDIRNet: Robust Radar Emitter Recognition via Single Domain Generalization

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Journal of Systems Engineering and Electronics 2025年第2期36卷 397-404页

作者： Wu Honglin Li Xueqiong Huang Junjie Jin Ruochun Tang Yuhua Institute for Quantum Information & State Key Laboratory of High Performance Computing National University of Defense Technology Changsha China

Automatically recognizing radar emitters from complex electromagnetic environments is important but non-trivial. Moreover, the changing electromagnetic environment results in inconsistent signal distribution in the real world, which makes the existing approaches perform poorly for recognition tasks in different scenes. In this paper, we propose a domain generalization framework is proposed to improve the adaptability of radar emitter signal recognition in changing environments. Specifically, we propose an end-to-end denoising based domain-invariant radar emitter recognition network (DDIRNet) consisting of a denoising model and a domain invariant representation learning model (IRLM), which mutually benefit from each other. For the signal denoising model, a loss function is proposed to match the feature of the radar signals and guarantee the effectiveness of the model. For the domain invariant representation learning model, contrastive learning is introduced to learn the cross-domain feature by aligning the source and unseen domain distribution. Moreover, we design a data augmentation method that improves the diversity of signal data for training. Extensive experiments on classification have shown that DDIRNet achieves up to 6.4% improvement compared with the state-of-the-art radar emitter recognition methods. The proposed method provides a promising direction to solve the radar emitter signal recognition problem.

关键词： Training Representation learning Adaptation models Noise reduction Radar Contrastive learning Data augmentation Signal denoising Modeling Electromagnetics

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Towards Radar Emitter Recognition in Changing Environments with Domain Generalization 2

Towards Radar Emitter Recognition in Changing Environments w...

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2nd International Conference on Signal Processing, Computer Networks and Communications, SPCNC 2023

作者： Wu, Honglin Li, Xueqiong Lan, Long Xu, Liyang Tang, Yuhua Institute for Quantum Information State Key Laboratory of High Performance Computing College of Computer Science and Technology National University of Defense Technology Changsha China

ISBN: (纸本)9798400716430

Analyzing radar signals from complex Electronic Warfare (EW) environment is a non-trivial task. However, in the real world, the changing EW environment results in inconsistent signal distribution, such as the pulse repetition interval (PRI) mismatch between different detected scenes. In this paper, we propose a novel domain generalization framework to improve the adaptability of signal recognition in changing environments. Specifically, we first design several noise generators to simulate varied scenes. Different from conventional augmentation methods, our introduced generators carefully enhance the diversity of the detected signals and meanwhile maintain the semantic features of the signals. Moreover, we propose a signal scene domain classifier that works in the manner of adversarial learning. The proposed classifier guarantees the signal predictor to generalize to different scenes. Extensive comparative experiments prove the proposed method's superiority. © 2023 ACM.

关键词： Electronic warfare

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Decomposition, Interaction, Reconstruction Meets Global Context Learning In Visual Tracking 48

Decomposition, Interaction, Reconstruction Meets Global Cont...

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48th IEEE International Conference on Acoustics, Speech and Signal Processing, ICASSP 2023

作者： Tan, Huibin Hu, Kun Cao, Mingyu Wang, Mengzhu Xu, Liyang Yang, Wenjing Institute for Quantum Information State Key Laboratory of High Performance Computing China National University of Defense Technology College of Computer Science and Technology Changsha China

ISBN: (纸本)9781728163277

Tensor decomposition and reconstruction attention is a promising global context learning approach because it can remain efficient while avoiding feature compression. To exploit its potential even further in visual tracking, we redesign a 3D tensor modeling paradigm, namely tensor Decomposition, Interaction, Reconstruction attention (DIR), respectively corresponding to three function components, Tensor Decomposition Module (TDM), Tensor Interaction Module (TIM) and Context Reconstruction Module (CRM). Specifically, TDM decomposes a 3D tensor feature into rank-1 context fragments in different dimension views. The ingenuity here lies in the introduction of Circular Convolution for processing features at arbitrary scales and channel-sharing segments to enhance the interaction of the two branches in the Siamese network architecture. TIM obtains the tensor planes of each dimension by the Cross-Similarity operation of rank-1 tensors and fused cubic features, which brings more interactions between all feature dimensions. CRM reconstructs 3D context representations with the outputs of the above modules. In experiments, DIR is embedded into the tracker to verify its effectiveness. © 2023 IEEE.

关键词： context reconstruction Siamese tracking single object tracking tensor plane

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Integrating Velox into TinkerPop for Graph Queries on Vectorized Engine 3

Integrating Velox into TinkerPop for Graph Queries on Vector...

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3rd International Conference on Electronic Information Engineering and Computer, EIECT 2023

作者： Li, Zihao Xu, Liyang Jin, Ruochun Chen, Huan Tang, Yuhua National University of Defense Technology Institute for Quantum Information & State Key Laboratory of High Performance Computing College of Computer Science and Technology Changsha410073 China

ISBN: (纸本)9798350357707

To enhance the query efficiency of relational databases and build a unified computing backend, Meta has developed Velox, a vectorized execution engine library based on columnar storage, Currently, there is no standardized specification for computation engine, and storage in graph databases, leading to failed to effectively utilize the vectorized processing capability of modern CPU. In this paper, we propose a middleware that primarily focuses on (1) non-invasively integrating Velox into the TinkerPop framework to provide unified vectorized engine acceleration for all graph databases supporting the TinkerPop specification;(2) conducting graph queries based on the relational data storage model, eliminating the overhead of transforming the storage model into a graph storage model;(3) validating the acceleration effect of the vectorized engine on interactive workload of graph queries under a single-node environment. © 2023 IEEE.

关键词： Engines

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