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Mobile robot path planning based on an improved ACO algorithm and path optimization

Multimedia Tools and Applications 2025年第12期84卷 10899-10922页

作者： Zhou, Tianfeng Wei, Wenhong School of Computer Science and Technology Dongguan University of Technology Dongguan523808 China

This paper will improve the fundamental ant colony optimization algorithm in the context of mobile robot path planning in response to its flaws, which include easy descent into a local optimum, a large number of inflection points along the path, and an inefficient convergence speed. Based on the two-dimensional grid map modeling approach, map pheromone initialization is unevenly assigned to improve the blindness of the ant colony optimization algorithm in the initial phase of path planning. Furthermore, an adaptive pheromone volatility factor is proposed to improve the global characteristics of early path planning, accelerate the convergence speed as the iteration advances, and avoid a local optimum that could impede the algorithm's progress. The pheromone update mechanism was redesigned, and a reward and punishment mechanism was incorporated into the pheromone update function to accelerate the convergence of the algorithm. Finally, path optimization diminishes the quantity of inflection points along the path, which makes the path smoother and shortens the path length. The outcome of the experiments demonstrate that the improved ant colony optimization algorithm performs better, converges more quickly, and more closely matches the needs of actual mobile robot walking. © The Author(s), under exclusive licence to Springer science+Business Media, LLC, part of Springer Nature 2024.

关键词： Mobile robots

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Energy Consumption Minimization for Delay-Sensitive Data Collection in AAV-Assisted WSN

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IEEE Sensors Journal 2025年第10期25卷 18394-18408页

作者： Liu, Xiaoying Zhou, Biao Tian, Xianzhong Gong, Weihua Zheng, Kechen Zhejiang University of Technology School of Computer Science and Technology Hangzhou310023 China

To address the issue of delay-sensitive data collection in wireless sensor networks (WSNs), autonomous aerial vehicles (AAVs) offer a promising solution due to their flexibility and maneuverability. We investigate the AAV-assisted WSN with delay-sensitive data, where sensor nodes (SNs) are distributed in monitoring areas (MAs) to sense the environment and generate data, AAVs are dispatched to collect the generated data from SNs and deliver it to a data center within a predetermined delay. Constrained by the limited onboard energy of AAVs, we minimize the total energy consumption of AAVs by jointly optimizing the grouping, transmit power, and bandwidth of SNs, the number, associated collection points (CPs), and flight trajectories of AAVs subject to the predetermined delay constraint. As the formulated minimization problem is NP-hard, we decompose it into two subproblems, i.e., the grouping, transmit power, and bandwidth of SNs subproblem, and the number, associated CPs, and flight trajectories of AAVs subproblem. To tackle the first subproblem, we propose a hybrid FDMA and NOMA (HFN) protocol that incorporates the optimal grouping of SNs scheme, derives the optimal transmit power of SNs, and proposes the low-complexity heap-based bandwidth optimization algorithm. To tackle the second subproblem, we propose a clustering-based trajectory and number of AAVs optimization (CTNO) algorithm that incorporates the low-complexity improved partitioning around medoids (IPAMs) algorithm and the high-efficiency improved tabu search (ITS) algorithm. Numerical results show the superior performance of the HFN protocol and CTNO algorithm in terms of the energy consumption of AAVs. © 2001-2012 IEEE.

关键词： Sensor nodes

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Multi-label, Classification-based Prediction of Breast Cancer Metastasis Directions

IAENG International Journal of Computer Science

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IAENG International Journal of computer science 2025年第1期52卷 1-10页

作者： Wang, Tingting Fan, Qi Tan, Liang Zhang, Beier School of Computer and Software Engineering Anhui Institute of Information Technology China School of Computer Science and Technology Huaibei Normal University China School of Computer and Software Engineering Anhui Institute of Information Technology China School of Computer Science and Technology Huaibei Normal University China

Predicting the metastatic direction of primary breast cancer (BC), thus assisting physicians in precise treatment, strict follow-up, and effectively improving the prognosis. The clinical data of 293,946 patients with primary BC diagnosed between 2010 and 2015 were collected from the Surveillance, Epidemiology, and End Results database. Multiple interpolations and Multi-label Synthetic Minority Over-sampling Technique methods were used for data analysis, and machine learning model was established for multi-label classification. Finally, Surgical information, lymph node status, distant metastasis, tumor size, chemotherapy, histological type, and radiotherapy had significant influence as inputs. Compared with the k-nearest neighbor model, average accuracies of the decision tree and random forest (RF) models increased from 88.84% to 93.59% and 94.14%, respectively. Their average precision, recall rate, F1 score, area under the receiver operating characteristic curve and weighted-F1 increased from 87.24% to 95.85% and 94.74%, 87.73% to 90.40% and 91.76%, 87.07% to 92.16% and 93.45%, 97.11% to 99.53% and 99.95%, 82.13% to 89.44% and 90.48%, respectively. In conclusion, the RF model, which showed the best performance, can be used in multi-label prediction of BC metastasis directions, and can assist physicians in diagnosing and treating patients with primary BC. © (2025), (International Association of Engineers). All rights reserved.

关键词： Lung cancer

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ConvGRU: A Lightweight Intrusion Detection System for Vehicle Networks Based on Shallow CNN and GRU

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IEEE Access 2025年 13卷 73297-73318页

作者： Wang, Shaoqiang Cheng, Jiahui Wang, Yizhe Li, Shutong Kang, Lei Dai, Yinfei Changchun University School of Computer Science and Technology Changchun130022 China Jilin University College of Computer Science and Technology Changchun130012 China

The rapid proliferation of connected vehicles has significantly expanded the attack surface of the Internet of Vehicles (IoV), introducing severe security risks. In such resource-constrained environments, developing lightweight solutions is crucial to ensuring real-time detection and efficient deployment. To address these challenges, this study proposes ConvGRU, a lightweight vehicular network intrusion detection model that integrates a shallow Convolutional Neural Network (CNN) with a Gated Recurrent Unit (GRU). By employing optimizations such as small convolutional kernels and depthwise separable convolutions, the model significantly reduces the number of parameters and computational overhead, making it well-suited for resource-limited IoV environments. The shallow CNN effectively captures spatial features, while the GRU extracts temporal dependencies, enhancing the model’s generalization ability. ConvGRU achieves an accuracy, precision, recall, and F1-score exceeding 0.99 on the HCRL-Car-hacking, OTIDS, and CICIDS-2018 datasets, with only 112.55K parameters and a memory footprint of merely 0.43 MB. Experimental results demonstrate that this intrusion detection solution substantially improves malicious traffic detection accuracy while ensuring efficient operation in resource-constrained vehicular environments. © 2013 IEEE.

关键词： Network intrusion

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B+-Tree-Quantization-Based Shared Secret Key Extraction Scheme From Atmospheric Optical Wireless Channel

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IEEE Transactions on Communications 2025年第5期73卷 3394-3408页

作者： Pan, Gang Chen, Chunyi Changchun University of Science and Technology School of Computer Science and Technology Changchun130022 China Changchun University of Science and Technology School of Computer Science and Technology Key Laboratory of Photoelectric Measurement & Control and Optical Information Transfer Technology Changchun130022 China Chongqing Research Institute Changchun University of Science and Technology Chongqing401135 China

To reduce key disagreement rate and increase key generation rate, this paper proposes a lightweight and robust shared secret key extraction scheme from atmospheric optical wireless channel. A conception of grouping samples by using B+ tree and quantizing groups is introduced to improve the balance of distribution of channel measurement values in quantization intervals. Based on the conception, a basic B+ tree quantization algorithm (B-BPTQ) and a compensation-technology-based B+ tree quantization algorithm (C-BPTQ) are designed. A LDPC-based multi-scale information reconciliation algorithm (LDPC-MSIR) is put forward to simplify information reconciliation and reduce its computation complexity. An enhanced randomness post-processing algorithm is formulated to increase statistical randomness of initial key sequence. Finally, the performance of the proposed scheme is validated by numerical simulations and field experiments. The experimental results show that it has better performance in terms of key disagreement rate, key generation rate and key randomness compared with other schemes. © 1972-2012 IEEE.

关键词： Optical communication

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Privacy-Preserving Statistical Analysis With Low Redundancy Over Task-Relevant Microdata

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IEEE Transactions on Information Forensics and Security 2025年 20卷 4382-4395页

作者： Zhao, Jingcheng Xue, Kaiping Xue, Yingjie Li, Meng Zhu, Bin Yuan, Shaoxian University of Science and Technology of China School of Cyber Science and Technology Anhui Hefei230027 China Hefei University of Technology School of Computer Science and Information Engineering Anhui Hefei230601 China

Privacy-preserving statistical analysis enables the data center to analyze datasets from multiple data owners, extracting valuable insights while safeguarding privacy. However, the observation of microdata involvement in various analysis tasks within the data center can indirectly lead to privacy breaches. For instance, when the data center observes microdata involved in a disease-related task, it may reveal information about the corresponding user’s disease. Existing schemes process the entire dataset for each analysis task to prevent privacy breaches, resulting in significant redundancy overhead due to the large amount of task-irrelevant data involved in processing. In this paper, we propose FDC, which can protect privacy and effectively reduce the redundancy overhead. It frees the data center from huge redundancy overhead. Specifically, we propose a co-design of local differential privacy and multiparty computation with preprocessing by the data owner. This design enables the data center to process only task-relevant and LDP noise-induced microdata instead of the entire dataset while maintaining analysis results without accuracy loss. In some scenarios where preprocessing by the data owner is unfeasible, we present a data center-assisted method to complete preprocessing within the data center. Additionally, we design and optimize a secure shuffle protocol within this method. Finally, we implement and evaluate FDC using the aggregation task as a baseline. With different proportions of task-relevant microdata, experimental results show that the runtime of FDC is 2\sim 11 x faster than existing schemes on LAN and 2\sim 22 x on WAN, and the communication overhead is up to 3\sim 153 x lower. © 2005-2012 IEEE.

关键词： Data centers

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Uncertainties of the standard quantum teleportation channel

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Chinese Physics B 2025年第4期34卷 273-285页

作者： Zhihua Zhang Zehao Guo Zhipeng Qiu School of Cyber Science and Engineering Nanjing University of Science and TechnologyJiangyin 214443China School of Mathematics and Statistics Nanjing University of Science and TechnologyNanjing 210094China

From the perspective of state-channel interaction,standard quantum teleportation can be viewed as a communication process characterized by both input and output,functioning as a quantum depolarizing *** achieve a precise quantification of the quantumness introduced by this channel,we examine its uncertainties,which encompass both statedependent and state-independent ***,for qudit systems,we provide general formulas for these *** analyze the uncertainties associated with standard quantum teleportation when induced by isotropic states,Werner states,and X-states,and we elucidate the correlation between these uncertainties and the parameters of the specific mixed *** findings demonstrate the validity of quantifying these uncertainties.

关键词： uncertainty standard quantum teleportation channel state-channel interaction

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Using DTL-MD with GANs and ResNet for Malicious Code Detection

Informatica (Slovenia)

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Informatica (Slovenia) 2025年第14期49卷 63-78页

作者： Li, Yiming Xie, Tao Mei, Dongdong Department of Computer Science and Technology School of Computer Science and Engineering Ningxia Institute of Science and Technology Shizuishan753000 China

This study proposes a malicious code detection model DTL-MD based on deep transfer learning, which aims to improve the detection accuracy of existing methods in complex malicious code and data scarcity. In the feature extraction process, the weighted sum method of GIST and LBP features is used to combine the advantages of the two features. Online transfer learning is used to reduce the data distribution difference between the target domain and the source domain. The model uses ResNet50V2 as the backbone network and combines SimAM to enhance the feature extraction and representation capabilities. In addition, in order to further improve the robustness of detection, GAN is used to generate malicious code variants and expand the training data set. In the experiment, the public CICIDS 2017 data set is used for model training and testing. The performance test results show that when the threshold is 0.7, the accuracy of DTL-MD is 95.8% and the F1 score is 0.93. In a performance test involving 30,000 samples, the throughput of the DTL-MD model under Trojans, viruses, worms, and adware is 11, 12, 11, and 12 tasks/s, respectively, and the inference time is 211, 225, 239, and 234 samples/s, respectively. Compared with GAN, DTL-MD increases the throughput by about 10% and the inference speed by about 15%. The research aims to provide new ideas for improving the intelligence and automation level of malicious code detection technology, which has certain application value and practical significance. © 2025 Slovene Society Informatika. All rights reserved.

关键词： Feature Selection

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RVAM16:a low-cost multiple-ISA processor based on RISC-V and ARM Thumb

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Frontiers of computer science 2025年第1期19卷 41-51页

作者： Libo HUANG Jing ZHANG Ling YANG Sheng MA Yongwen WANG Yuanhu CHENG College of Computer Science and Technology National University of Defense TechnologyChangsha 410073China

The rapid development of ISAs has brought the issue of software compatibility to the forefront in the embedded *** address this challenge,one of the promising solutions is the adoption of a multiple-ISA processor that supports multiple different ***,due to constraints in cost and performance,the architecture of a multiple-ISA processor must be carefully optimized to meet the specific requirements of embedded *** exploring the RISC-V and ARM Thumb ISAs,this paper proposes RVAM16,which is an optimized multiple-ISA processor microarchitecture for embedded devices based on hardware binary translation *** results show that,when running non-native ARM Thumb programs,RVAM16 achieves a significant speedup of over 2.73×with less area and energy consumption compared to using hardware binary translation alone,reaching more than 70%of the performance of native RISC-V programs.

关键词： multiple-ISA processor architecture binary translation RISC-V embedded

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Code context-based reviewer recommendation

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Frontiers of computer science 2025年第1期19卷 97-108页

作者： Dawei YUAN Xiao PENG Zijie CHEN Tao ZHANG Ruijia LEI School of Computer Science and Engineering Macao University of Science and TechnologyMacao 999078China Faculty of Science University of AmsterdamAmsterdam WB 1018Netherlands

Code review is a critical process in software development, contributing to the overall quality of the product by identifying errors early. A key aspect of this process is the selection of appropriate reviewers to scrutinize changes made to source code. However, in large-scale open-source projects, selecting the most suitable reviewers for a specific change can be a challenging task. To address this, we introduce the Code Context Based Reviewer Recommendation (CCB-RR), a model that leverages information from changesets to recommend the most suitable reviewers. The model takes into consideration the paths of modified files and the context derived from the changesets, including their titles and descriptions. Additionally, CCB-RR employs KeyBERT to extract the most relevant keywords and compare the semantic similarity across changesets. The model integrates the paths of modified files, keyword information, and the context of code changes to form a comprehensive picture of the changeset. We conducted extensive experiments on four open-source projects, demonstrating the effectiveness of CCB-RR. The model achieved a Top-1 accuracy of 60%, 55%, 51%, and 45% on the Android, OpenStack, QT, and LibreOffice projects respectively. For Mean Reciprocal Rank (MRR), CCB achieved 71%, 62%, 52%, and 68% on the same projects respectively, thereby highlighting its potential for practical application in code reviewer recommendation.

关键词： code reviewer recommendation code context pull request

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