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arXiv 2025年

作者： Alisic, Rijad Kim, Junsoo Sandberg, Henrik the Division of Decision and Control Systems KTH Royal Institute of Technology Sweden the Department of Electrical and Information Engineering Seoul National University of Science and Technology Korea Republic of

Detecting attacks using encrypted signals is challenging since encryption hides its information content. We present a novel mechanism for anomaly detection over Learning with Errors (LWE) encrypted signals without using decryption, secure channels, nor complex communication schemes. Instead, the detector exploits the homomorphic property of LWE encryption to perform hypothesis tests on transformations of the encrypted samples. The specific transformations are determined by solutions to a hard lattice-based minimization problem. While the test’s sensitivity deteriorates with suboptimal solutions, similar to the exponential deterioration of the (related) test that breaks the cryptosystem, we show that the deterioration is polynomial for our test. This rate gap can be exploited to pick parameters that lead to somewhat weaker encryption but large gains in detection capability. Finally, we conclude the paper by presenting a numerical example that simulates anomaly detection, demonstrating the effectiveness of our method in identifying attacks. Copyright © 2025, The Authors. All rights reserved.

关键词： Cryptography

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KN-LIO: Geometric Kinematics and Neural Field Coupled LiDAR-Inertial Odometry

arXiv

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arXiv 2025年

作者： Wang, Zhong Ren, Lele Wen, Yue Wang, Hesheng The Department of Automation School of Electronics Information and Electrical Engineering Shanghai Jiao Tong University Shanghai200240 China The School of Information and Control Engineering China University of Mining and Technology Xuzhou221008 China

Recent advancements in LiDAR-Inertial Odometry (LIO) have boosted a large amount of applications. However, traditional LIO systems tend to focus more on localization rather than mapping, with maps consisting mostly of sparse geometric elements, which is not ideal for downstream tasks. Recent emerging neural field technology has great potential in dense mapping, but pure LiDAR mapping is difficult to work on high-dynamic vehicles. To mitigate this challenge, we present a new solution that tightly couples geometric kinematics with neural fields to enhance simultaneous state estimation and dense mapping capabilities. We propose both semi-coupled and tightly coupled Kinematic-Neural LIO (KN-LIO) systems that leverage online SDF decoding and iterated error-state Kalman filtering to fuse laser and inertial data. Our KN-LIO minimizes information loss and improves accuracy in state estimation, while also accommodating asynchronous multi-LiDAR inputs. Evaluations on diverse high-dynamic datasets demonstrate that our KN-LIO achieves performance on par with or superior to existing state-of-the-art solutions in pose estimation and offers improved dense mapping accuracy over pure LiDAR-based methods. The relevant code and datasets will be made available at https://**. Copyright © 2025, The Authors. All rights reserved.

关键词： Mapping

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An Improved Object Detection Algorithm for UAV Images Based on Orthogonal Channel Attention Mechanism and Triple Feature Encoder

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IET Image Processing 2025年第1期19卷

作者： Wang, Wenfeng Wang, Chaomin Lei, Sheng Xie, Min Gui, Binbin Dong, Fang School of Information Engineering Nanchang Institute of Technology Nanchang China Jiangxi Province Key Laboratory of Smart Water Conservancy Nanchang Institute of Technology Nanchang China Flood Control Information Center Jiangxi Provincial Department of Water Resources Nanchang China

Object detection in Unmanned Aerial Vehicle (UAV) imagery plays an important role in many fields. However, UAV images usually exhibit characteristics different from those of natural images, such as complex scenes, dense small targets, and significant variations in target scales, which pose considerable challenges for object detection tasks. To address these issues, this paper presents a novel object detection algorithm for UAV images based on YOLOv8 (referred to as OATF-YOLO). First, an orthogonal channel attention mechanism is added to the backbone network to imporve the algorithm's ability to extract features and clear up any confusion between features in the foreground and background. Second, a triple feature encoder and a scale sequence feature fusion module are integrated into the neck network to bolster the algorithm's multi-scale feature fusion capability, thereby mitigating the impact of substantial differences in target scales. Finally, an inner factor is introduced into the loss function to further upgrade the robustness and detection accuracy of the algorithm. Experimental results on the VisDrone2019-DET dataset indicate that the proposed algorithm significantly outperforms the baseline model. On the validation set, the OATF-YOLO algorithm achieves a precision of 59.1%, a recall of 40.5%, an mAP50 of 42.5%, and an mAP50:95 of 25.8%. These values represent improvements of 3.8%, 3.0%, 4.1%, and 3.3%, respectively. Similarly, on the test set, the OATF-YOLO algorithm achieves a precision of 52.3%, a recall of 34.7%, an mAP50 of 33.4%, and an mAP50:95 of 19.1%, reflecting enhancements of 4.0%, 3.3%, 4.0%, and 2.6%, respectively. To further validate the model's robustness and scalability, experiments are conducted on the NWPU-VHR10 dataset, and OATF-YOLO also achieves excellent performance. Furthermore, compared to several classical object detection algorithms, OATF-YOLO demonstrates superior detection performance on both datasets and indicates that it is bette

关键词： Unmanned aerial vehicles (UAV)

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Blending Parametric Model and Neural Refinement: A Coarse-to-Fine Approach for Predicting Facial Changes in Orthognathic Surgery

Blending Parametric Model and Neural Refinement: A Coarse-to...

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IEEE International Symposium on Biomedical Imaging

作者： Jiawen Yang Dongming He Xuanyu Tian Lei Ma Jingyi Yu Xudong Wang Yuyao Zhang School of Information Science and Technology ShanghaiTech University Shanghai China Shanghai Ninth People's Hospital China Department of Control Science and Engineering School of Electronics and Information Engineering Tongji University Shanghai China

ISBN: (数字)9798331520526

ISBN: (纸本)9798331520533

Accurate prediction of postoperative facial soft tissue changes is crucial for effective preoperative planning in orthognathic deformity surgery. In this paper, we propose a novel coarse-to-fine framework for predicting these changes. The coarse module employs the parametric statistical face model, SCULPTOR, to generate an initial prediction of the postoperative face. SCULPTOR effectively captures the bidirectional linear relationship between the outer facial surface and underlying bone structures, while modeling surgical deformations. In the fine module, the coarse predictions are first converted into UV maps, which are then refined using FocalNet as the facial feature extractor (FFE), incorporating a craniofacial transformation module (CTM) to capture the non-linear relationship between the skeleton and facial surface. Experimental results on patients undergoing orthognathic surgery demonstrate that our method surpasses state-of-the-art techniques in predicting postoperative facial outcomes.

关键词： Surface reconstruction Accuracy Surgery Feature extraction Bones Parametric statistics Planning Reliability Facial features Faces

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Autotuned Non-Linear Extended-State-Observer Based Fixed-Time control for Motor-Drive Servo Systems

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IEEE Transactions on Power Electronics 2025年

作者： Xu, Ruiqi Liu, Jianxing Lin, Xinpo Liu, Zhuang Yan, Fei Gao, Yabin Harbin Institute of Technology Department of Control Science and Engineering Harbin150001 China Harbin Institute of Technology Department of Control Science and Engineering Weihai264209 China Southwest Jiaotong University School of Information Science and Technology Chengdu611756 China

To enhance the position tracking performance of permanent magnet synchronous motors (PMSMs) under unknown disturbances, this paper proposes a fast fixed-time control strategy based on an improved nonlinear extended state observer (NESO). First, load disturbances and nonlinear friction are considered as lumped disturbance, for which a NESO is designed to estimate the lumped disturbance. The control parameters of the NESO are tuned online using a neural network optimization algorithm, eliminating the need for offline training. Then, a robust fixed-time sliding mode control method is proposed, based on an improved nonsingular fast terminal sliding mode manifold, which offers better convergence performance. The Lyapunov method is used to prove the fixed-time stability of the position tracking error system. Finally, the effectiveness of the proposed method is validated on an experimental platform with PMSMs, and it is compared with other advanced fixed-time position control methods. The comparison results confirm that the proposed method exhibits superior control performance. © 1986-2012 IEEE.

关键词： State estimation

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Enhanced Security Through Honeywords and Video CAPTCHA: An Innovative Authentication and Tracking Mechanism 5

Enhanced Security Through Honeywords and Video CAPTCHA: An I...

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5th IEEE International Conference on Advances in Electrical, Computing, Communication and Sustainable Technologies, ICAECT 2025

作者： Gurale, Madhuri Borate, Komal Sangitrao, Rachana R. Tiwari, Garima D. Y. Patil College of Engineering Information Technology Department Akurdi Pune412101 India D. Y. Patil College of Engineering Artifical Intelligency and Data Science Department Akurdi Pune412101 India D. Y. Patil College of Engineering Department of Instrumentation and Control Akurdi Pune412101 India D. Y. Patil College of Engineering Department of Electronics & Communication Akurdi Pune412101 India

ISBN: (纸本)9798350356236

In recent years, activities across countries have increasingly been carried out digitally, with information being shared over networks to boost speed and efficiency. However, this digital transformation poses security threats. Cybercrime has consistently evolved, necessitating improved network security measures to combat digital scams. To maintain security, users are assigned unique identification passwords, which they are instructed to keep secret. Despite this, attackers often manage to steal passwords. To counter these threats, we propose using honeywords, which are decoy passwords created from the existing user password. If an attacker inputs a honeyword, an alert is activated for the administrator. Moreover, we implement IP and location monitoring for users and introduce a approach known as the video click-based CAPTCHA system. This scheme authenticates users by distinguishing between humans and bots, addressing issues in traditional graphic password CAPTCHA schemes. This architecture significantly enhances protection and security of data and applications over online networks. © 2025 IEEE.

关键词： Malware

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Designing 24 novel carbon allotropes in Pmmm space group: First-principles calculation

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Diamond and Related Materials 2025年 155卷

作者： Liu, Heng Xing, Mengjiang Li, Xiaozhen He, Fa Qin, Tianying Zeng, Yurong Fan, Qingyang School of Electronic Science and Engineering University of Electronic Science and Technology of China Chengdu611731 China State Key Laboratory of Electronic Thin Films and Integrated Devices University of Electronic Science and Technology of China Chengdu610054 China Department of Information and Technology Kunming University Kunming650214 China Quanzhou University of Information Engineering Quanzhou362000 China College of Information and Control Engineering Xi'an University of Architecture and Technology Xi'an710055 China

Utilizing advanced high-throughput computational techniques in conjunction with first-principles calculations, a total of 24 carbon allotropes belonging to the Pmmm space group (No. 47) have been successfully identified. Following the optimization of structures, the removal of redundant configurations, and rigorous stability assessments, 24 stable carbon allotropes with sp3 hybridization were finally determined from 107 initial candidates. Notably, 12 of these carbon phases possess superhard properties, and 6 were identified as direct bandgap semiconductors. The analysis revealed that carbon allotropes with greater densities tend to display advantageous properties, including lower enthalpy values, increased hardness, and broader bandgaps. Compared to previously reported carbon allotropes in the Pmmm space group, such as oP16, oP12, oP18, and oP72, the newly developed materials show significantly improved mechanical properties and enhanced stability. The bandgaps of the 24 newly discovered carbon allotropes predominantly range from 2.3 eV to 3.5 eV, with Pmmm C3 exhibiting the largest bandgap at 4.42 eV and Pmmm C16 presenting the smallest bandgap at 1.56 eV. Pmmm C3 not only exhibits outstanding hardness of 73 GPa and an extremely low enthalpy of 0.24 eV/atom but also has a very high Debye temperature of 2017 K. These novel carbon materials are expected to provide a crucial theoretical foundation for future experimental research work. © 2025

关键词： Brinell Hardness

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Anti-interference model of intelligent control for high-precision regulation of pressure in industrial low pressure area based on neural network

Anti-interference model of intelligent control for high-prec...

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2024 International Conference on Computer Application and information Security, ICCAIS 2024

作者： Ren, Yan Wang, Yunjiang Department of Information and Control Engineering Shenyang Institute of Science and Technology Liaoning Shenyang110167 China School of Mechatronics Engineering Shenyang Aerospace University Liaoning Shenyang110136 China

ISBN: (纸本)9781510689312

As science and technology progress, the traditional pressure regulation system is being phased out, and researchers today are focusing on the study of higher precision regulation systems as a crucial task. However, there are still problems with pressure regulation in low pressure areas. In this regard, a high-precision regulation model for low-pressure zone pressure based on neural networks is proposed, which applies the neural system to pressure regulation in low-pressure zones to achieve the purpose of high-precision regulation. Neural network is a computing model that imitates the structure and function of the human brain. It can achieve complex nonlinear mapping and intelligent processing by learning large amounts of data. Applying the nervous system to the field of pressure regulation can adaptively learn the load characteristics of the pressure system, thereby improving the accuracy and stability of load prediction. Real-time monitoring and fault diagnosis can also be performed on the operating status of the pressure system, thereby improving the reliability and safety of the system. And optimize and adjust the control strategy of the pressure system, so as to improve the efficiency and energy saving of the system. Based on this, this paper proposes to combine BP algorithm and particle swarm optimization algorithm to obtain PSOB model. stability. And it can adaptively adjust network parameters and control strategies, adapt to different working conditions and environmental disturbances, and improve control flexibility and intelligence. Historical data and real-time data can also be used for learning and updating to improve control accuracy and prediction capabilities. © The Authors.

关键词： Particle swarm optimization (PSO)

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Chattering Phenomena in Time-Optimal control for High-Order Chain-of-Integrator Systems With Full State Constraints

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IEEE Transactions on Automatic control 2025年

作者： Wang, Yunan Hu, Chuxiong Li, Zeyang Lin, Yujie Lin, Shize He, Suqin Department of Mechanical Engineering Tsinghua University Beijing China Mechatronic Control Engineering Zhejiang University Hangzhou China Laboratory for Information and Decision Systems Massachusetts Institute of Technology United States Department of Mathematical Sciences Tsinghua University Beijing China

Time-optimal control for high-order chain-of-integrator systems with full state constraints remains an open and challenging problem within the discipline of optimal control. The behavior of optimal control in high-order problems lacks precise characterization. Even the existence of the chattering phenomenon, i.e., the control switches infinitely many times over a finite period, remains unknown. This paper establishes a theoretical framework for chattering in the problem, providing novel findings on the uniqueness of state constraints inducing chattering, the upper bound of switching times in an unconstrained arc during chattering, and the convergence of states and costates to the chattering limit point. For the first time, this paper proves the existence of chattering in the considered problem. The chattering optimal control for 4th-order problems with velocity constraints is precisely solved, providing an approach to plan time-optimal snap-limited trajectories. Other cases of order 4 are proved not to allow chattering. The conclusions rectify a prevalent misconception in the industry concerning the time-optimality of S-shaped trajectories with minimal switching times. © 1963-2012 IEEE.

关键词： Optimal control systems

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Parkinson's disease tremor prediction towards real-time suppression: A self-attention deep temporal convolutional network approach

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Computers in Biology and Medicine 2025年 188卷 109814-109814页

作者： Tan, Guan Yuan Bakibillah, A.S.M. Chan, Ping Yi Tan, Chee Pin Nurzaman, Surya School of Engineering Monash University Bandar Sunway47500 Malaysia School of Information Technology Monash University Bandar Sunway47500 Malaysia Department of Systems and Control Engineering School of Engineering Institute of Science Tokyo Tokyo152-8552 Japan

Accurate prediction of Parkinson's disease tremor (PDT) is crucial for developing assistive technologies;however, this is challenging due to the nonlinear, stochastic, and nonstationary characteristics of PDT, which substantially vary among patients and their activities. Moreover, most models only have one-step prediction capabilities, which causes delays in real-time applications. This paper proposes a self-attention deep temporal convolutional network (SADTCN) model for the real-time prediction of hand-arm PDT signals from different activities and joint angular motions. The SADTCN can capture both short- and long-term dependencies and complex temporal and spatial dynamics of PDT signals and hence, can effectively adapt to varying tremor characteristics. The performance of the proposed model is evaluated using experimental hand-arm PDT data. The results show that the SADTCN outperforms existing deep learning (DL) models by accurately predicting varying tremor amplitudes and frequencies multi-step ahead. Moreover, we performed spectrum analysis on the measured and predicted signal using the short-time Fourier transform (STFT) as a measure of potential active tremor control and found that SADTCN can accurately determine the transience of tremor amplitude in frequency and time. Finally, we run the Wilcoxon signed-rank statistical test and the results show a statistically significant improvement in the proposed model over the other DL models in all conditions. Therefore, the SADTCN can overcome the nonstationary, nonlinear, and stochastic nature of PDT to perform multi-step prediction with high accuracy, robustness, and generalizability in unseen testing data. © 2025 The Authors

关键词： Prediction models

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