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Machine learning empowered UAV-based beamforming design in ISAC systems

science China(Information sciences) 2025年第5期68卷 285-286页

作者： Xiangyang DUAN Xiaoqi ZHANG Shuqiang XIA Zhongbin WANG Yihua MA Weijie YUAN State Key Laboratory of Mobile Network and Mobile Multimedia Technology Wireless and Computing Research InstituteZTE Corporation School of Electrical and Data Engineering University of Technology Sydney School of Automation and Intelligent Manufacturing Southern University of Science and Technology

Integrated sensing and communication (ISAC) has been proposed as an enabling technology for the realization of the next-generation wireless system,which focuses on performing wireless communication and sensing *** the various potential ISAC-based applications,unmanned aerial vehicle (UAV)-based ISAC plays a significant part in unlocking the potential of future next-generation wireless communication,facilitating low-latency data transmission in high-mobility *** by recent advancements,a variety of effective techniques have been investigated to optimize beamforming design in ISAC *** instance,the authors in [1] introduced an extended Kalman filtering (EKF)-based method tailored for millimeter wave (mmWave) ISAC ***,Ref.[2]proposed an extended interacting multiple model (IMM)-EKF framework designed for vehicular networks with intricate roadway *** these advancements,the aforementioned methods typically employ a separate scheme for channel prediction and beam alignment,which introduces additional signaling overhead in real-world ***,there is a demand for an end-to-end beamforming design approach specifically for UAV-based ISAC systems.

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Leveraging Blockchain for Development of Time Limit-Based Multi-authorized Secure Digital Signature Framework 5th

Leveraging Blockchain for Development of Time Limit-Based Mu...

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5th International Conference on Advances in Information Communication technology and Computing, AICTC 2024

作者： Mallikharjuna Rao, K. Kumar, Ashutosh Agrawal, Deepesh Reyya, Shikhar Data Science and Artificial Intelligence International Institute of Information Technology Naya Raipur Naya Raipur India Computer Science and Engineering International Institute of Information Technology Naya Raipur Naya Raipur India

ISBN: (纸本)9789819761050

This article deeply dives into a shift in document security by introducing innovative authentication measures. Conventional digital signature methods are less secure and can easily be forged. Our research explores the integration of multiple signatures authentication and time-constrained signature features, which will boost the security of digital signatures across documents;by introducing a time-constrained signing approach, we are addressing potential issues and establishing robust defense against various threats. Also, multiple files can be uploaded using a single CID in interplanetary file system (IPFS). Besides introducing multiple signers and time-limit features, our research utilizes IPFS for secure and collaborative document management. © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2025.

关键词： Authentication Protocol

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The Digital Twin City in Enhancing Flood Evacuation Systems: Future Opportunities and Challenges

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IEEE Access 2025年 13卷 32371-32383页

作者： Setijadi Prihatmanto, Ary Prasetyadi, Abdurrakhman Yoganingrum, Ambar Sutriadi, Ridwan Hadiana, Ana Bandung Institute of Technology School of Electrical and Informatics Engineering Bandung40132 Indonesia National Research and Innovation Agency Research Center for Data and Information Science Bandung40231 Indonesia Bandung Institute of Technology School of Architecture Planning and Policy Development Bandung40132 Indonesia

In recent decades, the increasing frequency and severity of natural disasters have necessitated the development of more effective disaster management systems. This paper explores the potential of digital twin technology to enhance flood evacuation systems. A digital twin-city model can simulate urban environments, predict flood inundation, and identify optimal evacuation routes in real-time. The study conducts a systematic literature review to assess current advancements, evaluating the integration of 3D city models, artificial intelligence, and Internet of things sensors in flood evacuation planning. Results indicate that digital twin technology significantly improves predictive capabilities and evacuation efficiency, although data integration, model accuracy, and computational demands remain. Future research should focus on enhancing data integration, refining simulation models, improving predictive analytics, developing advanced visualization tools, and validating models across diverse environments to ensure reliability and effectiveness. © 2025 The Authors.

关键词： Digital elevation model

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Gesture Showdown: Rock Paper Scissors with AI Vision

Gesture Showdown: Rock Paper Scissors with AI Vision

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2025 IEEE International Conference on Computational, Communication and Information technology, ICCCIT 2025

作者： Hemal Babu, H. Yeshwanth, C.D. Shravan, N. Bose, K. Regin Rajalakshmi Institute of Technology Department of Artificial Intelligence and Data Science Chennai India Rajalakshmi Institute of Technology Department of AI&DS and Cloud Computing Chennai India

ISBN: (纸本)9798331512965

This paper presents a gesture-based RockPaper-Scissors game that leverages computer vision and hand gesture recognition to enable intuitive human-computer interaction. Utilizing MediaPipe's pre-trained hand tracking module, the system detects and identifies 21 hand landmarks in real-time to classify gestures as rock, paper or scissors based on angular relationships between key points. OpenCV is used for webcam integration and video frame preprocessing, ensuring seamless interaction. The game compares the recognized gesture with a computer-generated move, dynamically displaying the results and resetting after each round for continuous play. The system identifies gestures through precise angle calculations, differentiating between closed fists, open palms and extended fingers. Sample outputs demonstrate real-time gameplay scenarios, highlighting gesture recognition accuracy and user engagement. Future enhancements include refining recognition accuracy, adding multiplayer functionality, introducing customizable gestures, and deploying the game on web and mobile platforms for broader accessibility. This project showcases the potential of integrating AI and computer vision in creating engaging and user-friendly applications. © 2025 IEEE.

关键词： Gesture recognition

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Block-gram:Mining knowledgeable features for efficiently smart contract vulnerability detection

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Digital Communications and Networks 2025年第1期11卷 1-12页

作者： Xueshuo Xie Haolong Wang Zhaolong Jian Yaozheng Fang Zichun Wang Tao Li Tianjin Key Laboratory of Network and Data Security Technology TianjinChina College of Computer Science Nankai UniversityTianjinChina Key Laboratory of Blockchain and Cyberspace Governance of Zhejiang Province China State Key Laboratory of Computer Architecture Institute of Computing TechnologyChinese Academy of SciencesChina

Smart contracts are widely used on the blockchain to implement complex transactions,such as decentralized applications on *** vulnerability detection of large-scale smart contracts is critical,as attacks on smart contracts often cause huge economic *** it is difficult to repair and update smart contracts,it is necessary to find the vulnerabilities before they are ***,code analysis,which requires traversal paths,and learning methods,which require many features to be trained,are too time-consuming to detect large-scale on-chain ***-based methods will obtain detection models from a feature space compared to code analysis methods such as symbol *** the existing features lack the interpretability of the detection results and training model,even worse,the large-scale feature space also affects the efficiency of *** paper focuses on improving the detection efficiency by reducing the dimension of the features,combined with expert *** this paper,a feature extraction model Block-gram is proposed to form low-dimensional knowledge-based features from ***,the metadata is separated and the runtime code is converted into a sequence of opcodes,which are divided into segments based on some instructions(jumps,etc.).Then,scalable Block-gram features,including 4-dimensional block features and 8-dimensional attribute features,are mined for the learning-based model ***,feature contributions are calculated from SHAP values to measure the relationship between our features and the results of the detection *** addition,six types of vulnerability labels are made on a dataset containing 33,885 contracts,and these knowledge-based features are evaluated using seven state-of-the-art learning algorithms,which show that the average detection latency speeds up 25×to 650×,compared with the features extracted by N-gram,and also can enhance the interpretability of the detection model.

关键词： Smart contract Bytecode&opcode Knowledgeable features Vulnerability detection Feature contribution

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A circuit area optimization of MK-3 S-box

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Cybersecurity 2025年第1期8卷 61-71页

作者： Yanjun Li Weiguo Zhang Yiping Lin Jian Zou Jian Liu Information Industry Information Security Evaluation Center The 15th Research Institute of China Electronic Technology Group CorporationBeijing 100083China Henan Key Laboratory of Network Cryptography Technology Zhengzhou 450001China Beijing Electronic Science and Technology Institute No.7 Fufeng RoadFengtai DistricBeijing 100083China College of Computer and Data Science Fuzhou UniversityFuzhou 350108China

In MILCOM 2015,Kelly et *** the authentication encryption algorithm MK-3,which applied the 16-bit *** paper aims to implement the 16-bit S-box with less circuit ***,we classifed the irreducible polynomials over F_(2n)into three *** we compared the logic gates required for multiplication over the fnite feld constructed by the three types of irreducible *** to the comparison result,we constructed the composite felds, F_((2^(4))^(2))and F_((2^(8))^(2)).Based on the isomorphism of fnite felds,the operations over F_(2)^(16)can be conducted over F_((2^(8))^(2)).Similarly,elements over F28 can be mapped to the corresponding elements over F_((2^(4)))^(2).Next,the SAT solver was used to optimize the operations over smaller feld F_(2)^(4).At last,the architecture of the optimized MK-3 S-box was worked *** with the implementation proposed by the original designer,the circuit area of the MK-3 S-box in this paper is reduced by at least 55.9%.

关键词： irreducible operations optimization

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Prediction of Liver Cirrhosis Disease by using XGBOOST Majority Voting Technique in Machine Learning 2

Prediction of Liver Cirrhosis Disease by using XGBOOST Major...

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2nd International Conference on Machine Learning and Autonomous Systems, ICMLAS 2025

作者： Saranya, Ms. K. Pandeeswari, N. KIT - KalaignarKarunanidhi Institute of Technology Department of Artificial Intelligence and Data Science Coimbatore India PSNA College of Engineering and Technology Department of Information Technology Dindigul India

ISBN: (纸本)9798331505745

Cirrhosis is a disease where chronic liver injury causes the liver to become extensively scarred. The liver's capacity to operate is compromised when healthy liver cells are replaced by scar tissue. Chronic infections with the hepatitis B (HBV) and hepatitis $C$ (HCV) viruses are the primary causes of cirrhosis, which can develop from viral hepatitis to cirrhosis over a period of 10 to 20 years. Alcohol consumption is another significant risk factor for developing cirrhosis. Liver disease is becoming increasingly common in India, with one out of every five adults affected. Liver-related deaths in India contribute significantly to the global number of liver-related deaths, with 268,580 deaths annually, accounting for 18.3% of global liver-related mortality. Surface nodularity is an important imaging biomarker for detecting cirrhosis. It refers to irregularities in the surface texture of the liver that can be observed through imaging techniques such as ultrasound or CT scans. The XGBoost algorithm has been proposed as an effective tool for detecting liver tumours and classifying liver disease stages. Specifically, to increase its accuracy, XGBoost is used with the majority voting technique. The study compares XGBoost to a number of machine learning methods, such as Support Vector Machine, AdaBoost, K-nearest neighbour and Naive Bayes, XG Boost outperforms these algorithms, with the maximum accuracy of 99.4% in detecting liver tumours. Classifying the stages of liver disease can assist diagnosis and treatment recommendations. The effectiveness of early intervention can be increased by classifying the stages of liver disease using machine learning algorithms. Our work achieves state-of-the-art performance in liver disease categorization by combining the majority voting technique with an enhanced XGBoost algorithm. The model demonstrated great classification accuracy when tested and trained on a Kaggle dataset of liver diseases. © 2025 IEEE.

关键词： Adaptive boosting

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A perspective on describing nucleonic flow and pionic observables within the ultra-relativistic quantum molecular dynamics model

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Nuclear science and Techniques 2025年第3期36卷 105-118页

作者： Yang-Yang Liu Jun-Ping Yang Yong-Jia Wang Qing-Feng Li Zhu-Xia Li Cheng-Jun Xia Ying-Xun Zhang China Nuclear Data Center China Institute of Atomic EnergyBeijing 102413China School of Science Huzhou UniversityHuzhou 313000China Institute of Modern Physics Chinese Academy of SciencesLanzhou 730000China Center for Gravitation and Cosmology College of Physical Science and TechnologyYangzhou UniversityYangzhou 225009China Guangxi Key Laboratory of Nuclear Physics and Technology Guangxi Normal UniversityGuilin 541004China

In this work,we study the impacts of the isospin-independent momentum-dependent interaction(MDI)and near-threshold NN→NΔcross sections(σ_(NN→NΔ))on the nucleonic flow and pion production observables in the ultra-relativistic quantum molecular dynamics(UrQMD)*** the updated isospin-independent MDI and the near-threshold NN→NΔcross sections in the Ur QMD model,17 observables,which are the directed flow(v_(1))and elliptic flow(v_(2))of neutrons,protons,Hydrogen(H),and charged particles as a function of transverse momentum(p_t∕A)or normalized rapidity(y^(lab)_0),and the observables constructed from them,the charged pion multiplicity(M(π))and its ratio(M(π^(-))∕M(π^(+))),can be simultaneously described at certain forms of symmetry *** refinement of the UrQMD model provides a solid foundation for further understanding the effects of the missed physics,such as the threshold effect,the pion potential,and the momentum-dependent symmetry *** constraints on the symmetry energy at the flow characteristic density 1.2±0.6ρ_(0)and the pion characteristic density 1.5±0.5ρ_(0)were obtained with the current version of UrQMD,and the corresponding symmetry energies were S(1.2ρ_(0))=34±4 MeV and S(1.5ρ_(0))=36±8 MeV,***,the discrepancies between the data and the calculated results of v_(2)^(n)and v_(2)^(9)at high p_(t)(rapidity)imply that the roles of the missing ingredients,such as the threshold effect,the pion potential,and the momentum-dependent symmetry potential,should be investigated by differential observables,such as the momentum and rapidity distributions of the nucleonic and pionic probes over a wide beam energy range.

关键词： Momentum dependent interaction NN→NΔcross section Symmetry energy Flow andπobservable

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An Entorhinal-Hippocampal Loop Model Based on Non-negative Sparse Coding

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Journal of The Institution of Engineers (India): Series B 2025年第1期106卷 113-127页

作者： Zhao, Kaixin Ren, Menghui College of Computer Science and Technology Henan Institute of Technology Xinxiang453003 China School of Electrical and Information Engineering Zhengzhou University Zhengzhou450001 China Manufacturing IoT Big Data Engineering Technology Research Center of Henan Province Xinxiang453003 China

In our study, we investigate how the brain maps environmental spaces into understandable maps through hippocampal place cells and entorhinal cortex grid cells. We uncover that the hippocampus and entorhinal cortex are not just directly connected but also engage in a sophisticated feedback loop crucial for spatial encoding during navigation. We developed an innovative network model based on this feedback mechanism, linking entorhinal inputs to hippocampal spatial encoding cells through a non-negative sparse coding, using grid cells and weak spatial cells as inputs. Our findings demonstrate that the model learns and adjusts the characteristics of spatial encoding cells, successfully learning the specific spatial selectivities of place cells even with inputs from cells carrying weaker spatial information. This highlights the critical role of feedback encoding in refining spatial representations, an indispensable component of the brain’s navigation system. Moreover, experiments comparing our model with others validate our model’s exceptional ability in generating precise spatial encodings, underscoring its superiority. This research not only deepens our understanding of the brain’s navigational mechanisms but also offers a robust computational framework for future neuroscience studies, emphasizing the intricate interplay between feedback loops and spatial encoding in the brain’s navigation system. © The Institution of Engineers (India) 2024.

关键词： Brain

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A study on Electronic Health Records (EHR) in India: Security challenges, initiatives and blockchain integration 4th

A study on Electronic Health Records (EHR) in India: Securit...

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4th International Conference on Computational Methods in science and technology, ICCMST 2024

作者： Bhargava, Misha Mahi, Samarjit Rao, Vidya Department of Data Science and Computer Applications Manipal Institute of Technology Manipal Academy of Higher Education Karnataka Manipal India

ISBN: (纸本)9781032911571

With its vast population and extensive healthcare requirements, India is moving toward digitalizing its health records. Safeguarding Electronic Health Records (EHRs) is crucial given the increase in security breaches and attacks, with India’s healthcare sector at the forefront. This study provides an overview of EHR security in India by exploring the EHR adoption rate in India and the barriers to its use. It focuses on the significant security challenges they bring, and the initiatives currently being implemented in India, such as the Ayushman Bharat Digital Mission (ABDM) and the recently released National Accreditation Board for Hospitals (NABH) Digital Health Standards. Integrating Blockchain in current EHR systems to resolve the observed challenges is then explored. The findings of this study show the urgency of integrating emerging technologies such as blockchain which have the potential for patient-controlled data sharing and data immutability possibly within the framework of the ABDM. This study aims to serve as a valuable resource for healthcare professionals, researchers and other regions seeking to understand and improve EHR security. © 2025 the Author(s).

关键词： Electronic health record

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