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arXiv 2023年

作者： Peng, Yi Zhang, Helen W.J. Zhong, Ying School of Mathematics Hunan University Changsha410082 China Hunan Provincial Key Laboratory of Intelligent Information Processing and Applied Mathematics Changsha410082 China

Bessenrodt and Ono, Chen, Wang and Jia, DeSalvo and Pak were the first to discover the log-subadditivity, log-concavity, and the third-order Turán inequality of partition function, respectively. Many other important partition statistics are proved to enjoy similar properties. This paper focuses on the partition function p̅k(n), which counts the number of overpartitions of n with no parts divisible by k. We provide a combinatorial proof to establish that for any k ≥ 2, the partition function p̅k(n) exhibits strict log-subadditivity. Specifically, we show that p̅k(a)pk(b) > p̅k(a + b) for integers a ≥ b ≥ 1 and a + b ≥ k. Furthermore, we investigate the log-concavity and the satisfaction of the third-order Turán inequality for p̅k(n), where 2 ≤ k ≤ 9. © 2023, CC BY.

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An Efficient Asynchronous Circuits Design Flow with Backward Delay Propagation Constraint

An Efficient Asynchronous Circuits Design Flow with Backward...

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Design, Automation and Test in Europe Conference and Exhibition

作者： Lingfeng Zhou Shanlin Xiao Huiyao Wang Jinghai Wang Zeyang Xu Bohan Wang Zhiyi Yu School of Microelectronics Science and Technology Sun Yat-sen University China Guangdong Provincial Key Laboratory of Optoelectronic Information Processing Chips and Systems China

ISBN: (数字)9783981926385

ISBN: (纸本)9798350348606

Asynchronous circuits have recently become more popular in Internet of Things (IoT) and neural network chips because of their potential low power consumption. However, due to the lack of Electronic Design Automation (EDA) tools, the asynchronous circuits design efficiency remains low and faces challenges in large-scale applications. This paper proposes a new asynchronous circuits design flow using traditional EDA tools, and applies a new backward delay propagation constraint (BDPC) method. In this method, control paths and data paths are tightly coupled and analyzed together to improve the accuracy of static timing analysis. Compared to previous works, the proposed design flow and constraint method offer significant advantages in terms of accuracy and efficiency. To verify this flow, an asynchronous RISC-V processor was implemented on TSMC 65nm process. Compared to synchronous version, asynchronous processor achieves a power optimization of 17.4 % while main-taining the same speed and area.

关键词： Electric potential Power demand Design automation Neural networks Delays Internet of Things Optimization

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Parking Slot Detection for Autonomous Parking System with Template Matching

Parking Slot Detection for Autonomous Parking System with Te...

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第42届中国控制会议

作者： Yijing Wang Runpeng Wang Zheng Li Zhiqiang Zuo Baowei Yu Tianjin Key Laboratory of Intelligent Unmanned Swarm Technology and System School of Electrical and Information EngineeringTianjin University

In this paper,a parking slot detection algorithm based on a bird's eye view is proposed.A density-based spatial clustering of applications with noise(DBSCAN) clustering algorithm and template matching algorithm are fused to detect parking slots in a bird's eye *** probabilistic Hough line detection and an improved DBS CAN clustering algorithm is developed to locate the sidelines of parking ***,template matching is provided to locate and classify the "T shape" and "L shape" marking points more ***,the marking points and sidelines of parking slots are integrated to complete the parking slot *** recall rate and precision rate of experimental results are 74.4% and 92.0%.

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Collaborative Consistency Autoencoder Hyperspectral Unmixing Using Deep Image Prior

Collaborative Consistency Autoencoder Hyperspectral Unmixing...

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IEEE International Symposium on Geoscience and Remote Sensing (IGARSS)

作者： Min Huang Mengxiong Tang Fan Li Shaoquan Zhang Shengqian Wang Ningyuan Zhang Chengzhi Deng Jiangxi Province Key Laboratory of Water Information Cooperative Sensing and Intelligent Processing Nanchang Institute of Technology Nanchang China Guangzhou Municipal Construction Group Co. LTD Guangzhou China

In the field of hyperspectral unmixing, deep learning has received increasing attention due to its powerful learning and data representation capabilities. Autoencoder is a popular technique for unmixing. Recently, an autoencoder-based depth image prior algorithm has been proposed for hyperspectral unmixing, which employs geometric methods to extract endmembers. The performance of this network solely focuses on estimating the abundance of images, and it has achieved good unmixing results. However, the depth image only employs one core network, which makes it highly vulnerable to noise and can lead to unstable unmixing outcomes. To address the aforementioned issue, this paper proposes a collaborative consistency autoencoder-based hyperspectral unmixing approach with deep image prior (CCAUDIP). For the proposed CCAUDIP model, it adopts two autoencoders to cooperatively handle the same input data to enhance the generalization ability of the network. Additionally, a consistency constraint is introduced to restrict the abundance outputs of the two core autoencoders and improve the robustness of the network. The experimental results show that the CCAUDIP method can achieve better unmixing results compared to other advanced unmixing algorithms.

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Consensus of Opinion Dynamics in Antagonistic Social Networks via a Delta Operator Approach

Consensus of Opinion Dynamics in Antagonistic Social Network...

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第42届中国控制会议

作者： Jiang Cao Peng Li Hongjiu Yang Jin Wang Tianjin Key Laboratory of Intelligent Unmanned Swarm Technology and System School of Electrical and Information EngineeringTianjin University

In this paper,a delta operator opinion dynamics model is established in social networks with antagonistic *** opinion interactions of all individuals in the antagonistic social networks are characterized as a delta operator dynamics system with *** is analyzed for the delta operator opinion dynamics model based on Lyapunov methods in delta *** results are given to illustrate effectiveness of the theoretical results for the delta operator opinion dynamics model.

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AIDetectVul: Software Vulnerability Detection Method Based on Feature Fusion of Pre-trained Models

AIDetectVul: Software Vulnerability Detection Method Based o...

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Consumer Electronics and Computer Engineering (ICCECE), 2021 IEEE International Conference on

作者： Shiying Xue Lin Li Tao Li Haodong Chen Jiapan Li Yangqing Qin School of Computer Science and Technology Wuhan University of Science and Technology Wuhan China Hubei Key Laboratory of Intelligent Information Processing and Real time Industrial Systems Wuhan University of Science and Technology Wuhan China Wuhan Jiaoyun Huizhi Information Technology Co. Ltd. Wuhan China Guotou Securities Co. Ltd. Shenzhen China

ISBN: (数字)9798331533113

ISBN: (纸本)9798331533120

Data-driven deep learning models are constrained by the scale and diversity of training data, making them vulnerable to data bias. While large language models (LLMs) exhibit superior generalization in vulnerability detection, their low inference efficiency and high computational costs hinder practical deployment in industrial settings. To address these limitations, we propose AIDetectVul, a novel vulnerability detection framework leveraging feature fusion from pre-trained models. Our approach concurrently utilizes encoder-only and decoder-only architectures to extract complementary code embeddings, with feature fusion enhancing semantic diversity. These enriched representations are then processed by a Transformer model, where the self-attention mechanism effectively captures long-range code dependencies, ultimately improving both detection accuracy and generalization capability. Comprehensive evaluations on proprietary enterprise datasets and open-source benchmarks demonstrate that AIDetectVul achieves comparable detection accuracy to the state-of-the-art LineVul model while demonstrating measurable improvements in generalization performance. Compared to LLM-based approaches, our solution maintains significantly lower computational overhead and training costs, making it particularly suitable for industrial applications.

关键词： Training Accuracy Codes Computational modeling Large language models Training data Computer architecture Feature extraction Transformers Vectors

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Image-Based Visual Servoing under Field of View Constraints for Robot Manipulators 12

Image-Based Visual Servoing under Field of View Constraints ...

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12th International Conference on CYBER technology in Automation, Control, and intelligent Systems, CYBER 2022

作者： Zuo, Guoyu Liu, Yan Huang, Gao Gong, Daoxiong Faculty of Information Beijing University of Technology Beijing100124 China Beijing Key Laboratory of Computing Intelligence and Intelligent Systems Beijing100124 China Beijing Advanced Innovation Center for Intelligent Robots and Systems Beijing100081 China

ISBN: (数字)9781665472678

ISBN: (纸本)9781665472678

The challenge in image-based visual servoing is to deal with the visibility constraints, which require the image features to always remain in the field of view (FOV) of the camera. In this paper, a novel constraint function is proposed to address this problem. The FOV constraint function represented by the coordinates of the features and the region boundaries defined in the image is added to the classical proportional controller, which leads the image features from the margin to the center of the image. Simulation results verify the efficiency of the modified controller by controlling a robot manipulator with eye-in-hand configuration. © 2022 IEEE.

关键词： Robot applications

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Modeling and Verification of processing Engines in SAR Imaging SoC

Modeling and Verification of Processing Engines in SAR Imagi...

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Signal, information and Data processing (ICSIDP), IEEE International Conference on

作者： Zhihan Zhang Chao Yang Yizhuang Xie Yongrui Li Ao Zhang Radar Research Lab School of Information and Electoronics Beijing Institute of Technology Beijing Key Laboratory of Embedded Real-Time Information Processing Technology Beijing China Shanghai Institute of Satellite Engineering Shanghai China

ISBN: (数字)9798331515669

ISBN: (纸本)9798331515676

With the development of real-time requirements for Synthetic Aperture Radar (SAR) imaging technology, SAR imaging processing SoC (System on Chip) has become an important research field at present. The subsystem for SAR imaging processing in the SAR imaging SoC needs to be fully verified during the development process. This article designs a UVM-based random verification environment, focusing on the verification requirements of key computing engines and interaction engines in the SAR imaging processing SoC. At the same time, a modeling method for computing engines and interaction engines is proposed, which can quickly and accurately describe the functions and behaviors of processing engines. Collaborating with the verification environment and the reference C model, the design under test achieves satisfactory coverage results. Consequently, the functionality of the processing engines within the subsystem can be comprehensively and effectively verified. The content studied in this article has certain value for the engineering verification implementation of SAR imaging algorithms in the SoC.

关键词： Computational modeling Focusing Streaming media Radar polarimetry Data models Real-time systems Timing System-on-chip Engines Synthetic aperture radar

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Design of LEO-MIMO Precoding Update Algorithm Based on Time Sliding Window

Design of LEO-MIMO Precoding Update Algorithm Based on Time ...

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Electronic Communication and Artificial Intelligence (IWECAI), International Workshop on

作者： Zewei Hu Lin Zheng Chao Yang School of Information and Communication Guilin University of Electronic Technology Guilin China Guangxi Key Laboratory of Wireless Wideband Communication and Signal Processing Guilin China

ISBN: (数字)9798350386943

ISBN: (纸本)9798350386950

In the Low Earth Orbit (LEO) satellite Internet of Things (IoT), there is a significant imbalance in the distribution of ground terminal locations. Under a fixed beam regime, this leads to scenarios where satellite network capacity is simultaneously overloaded and underutilized, resulting in low capacity utilization rates for LEO satellite-to-earth communications. Distributed LEO-MIMO (Low Earth Orbit-Multiple Input Multiple Output) can alleviate overloads and distribute capacity by extending the satellite beam coverage area. However, unlike terrestrial distributed MIMO, which faces co-channel interference issues, distributed satellite networking in LEO systems also deals with the heterogeneity of Doppler shifts across different satellite links. To address these issues, a distributed LEO-MIMO linear precoding update algorithm is proposed. This paper, utilizing spectral graph theory, reduces the problem of constructing a single-frame precoding matrix to a graph signal correlation calculation problem. We introduce a precoding solving algorithm based on a time sliding window, which enhance data processing efficiency and reduce communication latency.

关键词： Multiplexing Doppler shift Satellites Correlation Precoding Low earth orbit satellites Graph theory

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An Enhanced Deep Reinforcement Learning-Based Global Router for VLSI Design

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Wireless Communications and Mobile Computing 2023年第1期2023卷

作者： Xu, Saijuan Yang, Liliang Liu, Genggeng Department of Information Engineering Fujian Business University Fuzhou China College of Computer and Data Science Fuzhou University Fuzhou China Key Laboratory of Network Computing and Intelligent Information Processing Fuzhou University Fuzhou China

Global routing is a crucial step in the design of Very Large-Scale Integration (VLSI) circuits. However, most of the existing methods are heuristic algorithms, which cannot conjointly optimize the subproblems of global routing, resulting in congestion and overflow. In response to this challenge, an enhanced Deep Reinforcement Learning- (DRL-) based global router has been proposed, which comprises the following effective strategies. First, to avoid the overestimation problem generated by Q-learning, the proposed global router adopts the Double Deep Q-Network (DDQN) model. The DDQN-based global router has better performance in wire length optimization and convergence. Second, to avoid the agent from learning redundant information, an action elimination method is added to the action selection part, which significantly enhances the convergence performance of the training process. Third, to avoid the unfair allocation problem of routing resources in serial training, concurrent training is proposed to enhance the routability. Fourth, to reduce wire length and disperse routing resources, a new reward function is proposed to guide the agent to learn better routing solutions regarding wire length and congestion standard deviation. Experimental results demonstrate that the proposed algorithm outperforms others in several important performance metrics, including wire length, convergence performance, routability, and congestion standard deviation. In conclusion, the proposed enhanced DRL-based global router is a promising approach for solving the global routing problem in VLSI design, which can achieve superior performance compared to the heuristic method and DRL-based global router. © 2023 Saijuan Xu et al.

关键词： Reinforcement learning

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